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Artificial intelligenceFrom Wikipedia, the free encyclopedia"AI" redirects here. For other uses, see?Ai?and?Artificial intelligence (disambiguation).Artificial intelligence?(AI) is the?intelligence?exhibited by machines or software. It is an academic?field of study?which generally studies the goal of emulating human-like intelligence.?John McCarthy, who coined the term in 1955, HYPERLINK "" \l "cite_note-Coining_of_the_term_AI-1" [1]?defines it as "the science and engineering of making intelligent machines".[2]AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other.[3]?Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific?problems. Others focus on one of several possible?approaches?or on the use of a particular?tool?or towards the accomplishment of particular?applications.The central problems (or goals) of AI research include?reasoning,?knowledge, planning,?learning,?natural language processing?(communication),?perception?and the ability to move and manipulate objects.[4]?General intelligence?is still among the field's long term goals.[5]?Currently popular approaches include?statistical methods,?computational intelligence?andtraditional symbolic AI. There are a large number of tools used in AI, including versions of?search and mathematical optimization,?logic,?methods based on probability and economics, and many others. The AI field is interdisciplinary, in which a number of sciences and professions converge, including?computer science,?psychology,?linguistics,?philosophy?andneuroscience, as well as other specialized field such as?artificial psychology.The field was founded on the claim that a central property of humans, intelligence—the?sapience?of?Homo sapiens—"can be so precisely described that a machine can be made to simulate it."[6]?This raises philosophical issues about the nature of themind?and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been addressed bymyth,?fiction?and?philosophy?since antiquity.[7]?Artificial intelligence has been the subject of tremendous optimism HYPERLINK "" \l "cite_note-8" [8]?but has also suffered stunning?setbacks.[9]?Today it has become an essential part of the technology industry, providing the heavy lifting for many of the most challenging problems in computer science.[10]AI could lead to?technological singularity. Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization in an event called the singularity.[11]?Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.[12]Contents??[hide]?1?History2?Goals2.1?Deduction, reasoning, problem solving2.2?Knowledge representation2.3?Planning2.4?Learning2.5?Natural language processing (communication)2.6?Perception2.7?Motion and manipulation2.8?Long-term goals2.8.1?Social intelligence2.8.2?Creativity2.8.3?General intelligence3?Approaches3.1?Cybernetics and brain simulation3.2?Symbolic3.3?Sub-symbolic3.4?Statistical3.5?Deep learning3.6?Integrating the approaches4?Tools4.1?Search and optimization4.2?Logic4.3?Probabilistic methods for uncertain reasoning4.4?Classifiers and statistical learning methods4.5?Neural networks4.6?Control theory4.7?Languages5?Evaluating progress6?Applications6.1?Competitions and prizes6.2?Platforms7?Philosophy8?Predictions and ethics9?In fiction10?See also11?References11.1?Notes12?References12.1?AI textbooks12.2?History of AI12.3?Other sources13?Further reading14?External linksHistory[ HYPERLINK "" \o "Edit section: History" edit]Main articles:?History of artificial intelligence?and?Timeline of artificial intelligenceThinking machines and artificial beings appear in?Greek myths, such as?Talos?of?Crete, the bronze robot of?Hephaestus, andPygmalion's?Galatea.[13]?Human likenesses believed to have intelligence were built in every major civilization: animated?cult images?were worshiped in?Egypt?and?Greece[14]?and humanoid?automatons?were built by?Yan Shi,?Hero of Alexandria?and?Al-Jazari.[15]?It was also widely believed that artificial beings had been created by?Jābir ibn Hayyān,?Judah Loew?andParacelsus.[16]?By the 19th and 20th centuries, artificial beings had become a common feature in fiction, as in?Mary Shelley'sFrankenstein?or?Karel ?apek's?R.U.R. (Rossum's Universal Robots).[17]?Pamela McCorduck?argues that all of these are some examples of an ancient urge, as she describes it, "to forge the gods".[7]?Stories of these creatures and their fates discuss many of the same hopes, fears and?ethical concerns?that are presented by artificial intelligence.Mechanical or?"formal" reasoning?has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of the?programmable digital electronic computer, based on the work of mathematician?Alan Turing?and others. Turing's?theory of computation?suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction.[18][19]?This, along with concurrent discoveries in?neurology,information theory?and?cybernetics, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.[20]The field of AI research was founded at?a conference?on the campus of?Dartmouth College?in the summer of 1956.[21]?The attendees, including?John McCarthy,?Marvin Minsky,?Allen Newell?and?Herbert Simon, became the leaders of AI research for many decades.[22]?They and their students wrote programs that were, to most people, simply astonishing: HYPERLINK "" \l "cite_note-23" [23]?computers were solving word problems in algebra, proving logical theorems and speaking English.[24]?By the middle of the 1960s, research in the U.S. was heavily funded by the?Department of Defense[25]?and laboratories had been established around the world.[26]AI's founders were profoundly optimistic about the future of the new field:?Herbert Simon?predicted that "machines will be capable, within twenty years, of doing any work a man can do" and?Marvin Minsky?agreed, writing that "within a generation?... the problem of creating 'artificial intelligence' will substantially be solved".[27]They had failed to recognize the difficulty of some of the problems they faced.[28]?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 all undirected exploratory research in AI. The next few years would later be called an "AI winter", HYPERLINK "" \l "cite_note-First_AI_winter-29" [29]?a period when funding for AI projects was hard to find.In the early 1980s, AI research was revived by the commercial success of?expert systems, HYPERLINK "" \l "cite_note-Expert_systems-30" [30]?a form of AI program that simulated the knowledge and analytical skills of one or more 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 in the field.[31]?However, beginning with the collapse of the?Lisp Machine?market in 1987, AI once again fell into disrepute, and a second, longer lasting?AI winter?began.[32]In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence is used for logistics,?data mining,?medical diagnosis?and many other areas throughout the technology industry.[10]?The success was due to several factors: the increasing computational power of computers (see?Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and a new commitment by researchers to solid mathematical methods and rigorous scientific standards.[33]On 11 May 1997,?Deep Blue?became the first computer chess-playing system to beat a reigning world chess champion, HYPERLINK "" \o "Garry Kasparov" Garry Kasparov.[34]?In 2005, a Stanford robot won the?DARPA Grand Challenge?by driving autonomously for 131 miles along an unrehearsed desert trail.[35]?Two years later, a team from?CMU?won the?DARPA Urban Challenge?when their vehicle autonomously navigated 55 miles in an urban environment while adhering to traffic hazards and all traffic laws.[36]?In February 2011, in 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.[37]?The?Kinect, which provides a 3D body–motion interface for the?Xbox 360?and the Xbox One, uses algorithms that emerged from lengthy AI research HYPERLINK "" \l "cite_note-38" [38]?as does the iPhone's?Siri.Goals[ HYPERLINK "" \o "Edit section: Goals" edit]The general problem of simulating (or creating) intelligence has been broken down into a number of specific sub-problems. These consist of particular traits or capabilities that researchers would like an intelligent system to display. The traits described below have received the most attention.[4]Deduction, reasoning, problem solving[ HYPERLINK "" \o "Edit section: Deduction, reasoning, problem solving" edit]Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[39]?By the late 1980s and 1990s, AI research had also developed highly successful methods for dealing with?uncertain?or incomplete information, employing concepts from?probability?and economics.[40]For difficult problems, most of these algorithms can require enormous computational resources – most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem-solving algorithms is a high priority for AI research.[41]Human beings solve most of their problems using fast, intuitive judgements rather than the conscious, step-by-step deduction that early AI research was able to model.[42]?AI has made some progress at imitating this kind of "sub-symbolic" problem solving:?embodied agent?approaches emphasize the importance of?sensorimotor?skills to higher reasoning;?neural net?research attempts to simulate the structures inside the brain that give rise to this skill;?statistical approaches to AI?mimic the probabilistic nature of the human ability to guess.Knowledge representation[ HYPERLINK "" \o "Edit section: Knowledge representation" edit]An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.Main articles:?Knowledge representation?and?Commonsense knowledgeKnowledge representation[43]?and?knowledge engineering[44]?are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects;[45]?situations, events, states and time;[46]?causes and effects;[47]?knowledge about knowledge (what we know about what other people know);[48]?and many other, less well researched domains. A representation of "what exists" is an?ontology: the set of objects, relations, concepts and so on that the machine knows about. The most general are called?upper ontologies, which attempt to provide a foundation for all other knowledge.[49]Among the most difficult problems in knowledge representation are:Default reasoning?and the?qualification problemMany of the things people know take the form of "working assumptions." For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about all birds.?John McCarthy?identified this problem in 1969[50]?as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.[51]The breadth of?commonsense knowledgeThe number of atomic facts that the average person knows is astronomical. Research projects that attempt to build a complete knowledge base of?commonsense knowledge?(e.g.,?Cyc) require enormous amounts of laborious?ontological engineering?— they must be built, by hand, one complicated concept at a time.[52]?A major goal is to have the computer understand enough concepts to be able to learn by reading from sources like the internet, and thus be able to add to its own ontology.[ HYPERLINK "" \o "Wikipedia:Citation needed" citation needed]The subsymbolic form of some?commonsense knowledgeMuch of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"[53]?or an art critic can take one look at a statue and instantly realize that it is a fake.[54]?These are intuitions or tendencies that are represented in the brain non-consciously and sub-symbolically.[55]?Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that?situated AI,?computational intelligence, or?statistical AI?will provide ways to represent this kind of knowledge.[55]Planning[ HYPERLINK "" \o "Edit section: Planning" edit]A?hierarchical control system?is a form of?control system?in which a set of devices and governing software is arranged in a hierarchy.Main article:?Automated planning and schedulingIntelligent agents must be able to set goals and achieve them.[56]?They need a way to visualize the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the?utility?(or "value") of the available choices.[57]In classical planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be.[58]However, if the agent is not the only actor, it must periodically ascertain whether the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty.[59]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.[60]Learning[ HYPERLINK "" \o "Edit section: Learning" edit]Main article:?Machine learningMachine learning is the study of computer algorithms that improve automatically through experience HYPERLINK "" \l "cite_note-61" [61][62]?and has been central to AI research since the field's inception.[63]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. In?reinforcement learning[64]the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms of?decision theory, using concepts like?utility. The mathematical analysis of machine learning algorithms and their performance is a branch oftheoretical computer science?known as?computational learning theory.[65]Within?developmental robotics, developmental learning approaches were elaborated for lifelong cumulative acquisition of repertoires of novel skills by a robot, through autonomous self-exploration and social interaction with human teachers, and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.[66][67][68][69]Natural language processing (communication)[ HYPERLINK "" \o "Edit section: Natural language processing (communication)" edit]A?parse tree?represents thesyntactic?structure of a sentence according to some?formal grammar.Main article:?Natural language processingNatural language processing[70]?gives machines the ability to read and?understandthe languages that humans speak. 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 includeinformation retrieval?(or?text mining) and?machine translation.[71]A common method of processing and extracting meaning from natural language is through semantic indexing. Increases in processing speeds and the drop in the cost of data storage makes indexing large volumes of abstractions of the user's input much more efficient.Perception[ HYPERLINK "" \o "Edit section: Perception" edit]Main articles:?Machine perception,?Computer vision?and?Speech recognitionMachine perception[72]?is the ability to use input from sensors (such as cameras, microphones,?tactile sensors, sonar and others more exotic) to deduce aspects of the world.?Computer vision[73]?is the ability to analyze visual input. A few selected subproblems are?speech recognition,[74]?facial recognition?and?object recognition.[75]Motion and manipulation[ HYPERLINK "" \o "Edit section: Motion and manipulation" edit]Main article:?RoboticsThe field of?robotics[76]?is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation[77]?and?navigation, with sub-problems of?localization?(knowing where you are, or finding out where other things are),?mapping?(learning what is around you, building a map of the environment), and?motion planning?(figuring out how to get there) or path planning (going from one point in space to another point, which may involve compliant motion - where the robot moves while maintaining physical contact with an object).[78][79]Long-term goals[ HYPERLINK "" \o "Edit section: Long-term goals" edit]Among the long-term goals in the research pertaining to artificial intelligence are: (1) Social intelligence, (2) Creativity, and (3) General intelligence.Social intelligence[ HYPERLINK "" \o "Edit section: Social intelligence" edit]Main article:?Affective computingKismet, a robot with rudimentary social skills HYPERLINK "" \l "cite_note-80" [80]Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human?affects.[81][82]?It is an interdisciplinary field spanning?computer sciences,?psychology, and?cognitive science.[83]?While the origins of the field may be traced as far back as to early philosophical inquiries into?emotion,[84]?the more modern branch of computer science originated with?Rosalind Picard's 1995 paper[85]?on affective computing.[86][87]?A motivation for the research is the ability to simulate?empathy. The machine should interpret the emotional state of humans and adapt its behaviour to them, giving an appropriate response for those emotions.Emotion and social skills HYPERLINK "" \l "cite_note-Emotion_and_affective_computing-88" [88]?play two roles for an intelligent agent. First, it must be able to predict the actions of others, by understanding their motives and emotional states. (This involves elements of?game theory,?decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions.) Also, in an effort to facilitate?human-computer interaction, an intelligent machine might want to be able to?display?emotions—even if it does not actually experience them itself—in order to appear sensitive to the emotional dynamics of human interaction.Creativity[ HYPERLINK "" \o "Edit section: Creativity" edit]Main article:?Computational creativityA sub-field of AI addresses?creativity?both theoretically (from a philosophical and psychological perspective) and practically (via specific implementations of systems that generate outputs that can be considered creative, or systems that identify and assess creativity). Related areas of computational research are?Artificial intuition?and Artificial thinking.General intelligence[ HYPERLINK "" \o "Edit section: General intelligence" edit]Main articles:?Artificial general intelligence?and?AI-completeMany researchers think that their work will eventually be incorporated into a machine with?general?intelligence (known asstrong AI), combining all the skills above and exceeding human abilities at most or all of them.[5]?A few believe thatanthropomorphic?features like?artificial consciousness?or an?artificial brain?may be required for such a project.[89][90]Many of the problems above may require general intelligence to be considered solved. For example, even a straightforward, specific task like?machine translation?requires that the 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 intention (social intelligence). A problem like?machine translation?is considered "AI-complete". In order to solve this particular problem, you must solve all the problems.[91]Approaches[ HYPERLINK "" \o "Edit section: Approaches" edit]There is no established unifying theory or?paradigm?that guides AI research. Researchers disagree about many issues.[92]?A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying?psychology?or?neurology? Or is human biology as irrelevant to AI research as bird biology is to?aeronautical engineering?[93]?Can intelligent behavior be described using simple, elegant principles (such as?logic?oroptimization)? Or does it necessarily require solving a large number of completely unrelated problems?[94]?Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require "sub-symbolic" processing?[95]?John Haugeland, who coined the term GOFAI (Good Old-Fashioned Artificial Intelligence), also proposed that AI should more properly be referred to as?synthetic intelligence,[96]?a term which has since been adopted by some non-GOFAI researchers.[97][98]Cybernetics and brain simulation[ HYPERLINK "" \o "Edit section: Cybernetics and brain simulation" edit]Main articles:?Cybernetics?and?Computational neuroscienceIn the 1940s and 1950s, a number of researchers explored the connection between?neurology,?information theory, andcybernetics. 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.[20]?By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.Symbolic[ HYPERLINK "" \o "Edit section: Symbolic" edit]Main article:?GOFAIWhen 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 each one developed its own style of research.?John Haugeland?named these approaches to AI "good old fashioned AI" or "GOFAI".[99]?During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on?cybernetics?or?neural networks?were abandoned or pushed into the background.[100]?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.Cognitive simulationEconomist?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?andmanagement 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 Universitywould eventually culminate in the development of the?Soar?architecture in the middle 1980s.[101][102]Logic-basedUnlike?Newell?and?Simon,?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.[93]?His laboratory at?Stanford?(SAIL) focused on using formal?logic?to solve a wide variety of problems, including?knowledge representation,?planning?and?learning.[103]?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.[104]"Anti-logic" or "scruffy"Researchers at?MIT?(such as?Marvin Minsky?and?Seymour Papert)[105]?found that solving difficult problems in?vision?andnatural 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).[94]?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.[106]Knowledge-basedWhen computers with large memories became available around 1970, researchers from all three traditions began to build?knowledge?into AI applications.[107]?This "knowledge revolution" led to the development and deployment of?expert systems?(introduced by?Edward Feigenbaum), the first truly successful form of AI software.[30]?The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.Sub-symbolic[ HYPERLINK "" \o "Edit section: Sub-symbolic" edit]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.[95]Bottom-up,?embodied,?situated,?behavior-based?or?nouvelle AIResearchers 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.[108]?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 putational intelligenceInterest in?neural networks?and "connectionism" was revived by?David Rumelhart?and others in the middle 1980s.[109]These and other sub-symbolic approaches, such as?fuzzy systems?and?evolutionary computation, are now studied collectively by the emerging discipline of?computational intelligence.[110]Statistical[ HYPERLINK "" \o "Edit section: Statistical" edit]In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly?scientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like?mathematics, economics or?operations research).?Stuart Russell?and?Peter Norvig?describe this movement as nothing less than a "revolution" and "the victory of the?neats."[33]?Critics argue that these techniques (with few exceptions HYPERLINK "" \l "cite_note-111" [111]) are too focused on particular problems and have failed to address the long term goal of general intelligence.[112]?There is an ongoing debate about the relevance and validity of statistical approaches in AI, exemplified in part by exchanges between?Peter Norvig?and?Noam Chomsky.[113][114]Deep learning[ HYPERLINK "" \o "Edit section: Deep learning" edit]An editor has expressed a concern that this section?lends?undue weight?to certain ideas relative to the article as a whole. Please help to?discuss?andresolve?the dispute before removing this message.?(September 2014)Deep learning?architectures, specifically those built from?artificial neural networks?(ANN), date back at least to theNeocognitron?introduced by?Kunihiko Fukushima?in 1980.[115]?The ANNs themselves date back even further. In 1989,?Yann LeCun?et al. were able to apply the standard?backpropagation?algorithm, which had been around since 1974,[116]?to a deep neural network with the purpose of recognizing handwritten?ZIP codes?on mail. Despite the success of applying the algorithm, the time to train the network on this dataset was approximately 3 days, making it impractical for general use.[117]Many factors contribute to the slow speed, one being due to the so-called?vanishing gradient problem?analyzed in 1991 byJürgen Schmidhuber's student?Sepp Hochreiter.[118][119]?In combination with speed issues, ANNs fell out of favor in practical machine learning and simpler models such as?support vector machines?(SVMs) became the popular choice of the field in the 1990s and 2000s.The term "deep learning" gained traction in the mid-2000s after a publication by?Geoffrey Hinton?and Ruslan Salakhutdinov showed how a many-layered?feedforward neural network?could be effectively pre-trained one layer at a time, treating each layer in turn as an?unsupervised?restricted Boltzmann machine, then using?supervised?backpropagation?for fine-tuning.[120]In 1992, Schmidhuber had already implemented a very similar idea for the more general case of unsupervised deep hierarchies of?recurrent neural networks, and also experimentally shown its benefits for speeding up supervised learning[121][122]Since the resurgence of deep learning, it has become part of many state-of-the-art systems in different disciplines, particularly that of computer vision and?automatic speech recognition?(ASR). Results on commonly used evaluation sets such as?TIMIT?(ASR) and?MNIST?(image classification) are constantly being improved with new applications of deep learning. Currently, it has been shown that deep learning architectures in the form of?convolutional neural networks?have been best performing, however, these are more widely used in computer vision than in ASR.Advances in hardware have also been an important enabling factor for the renewed interest of deep learning. In particular, powerful?graphics processing units?(GPUs) are highly suited for the kind of number crunching, matrix/vector math involved in machine learning. GPUs have been shown to speed up training algorithms by orders of magnitude, bringing running times of weeks back to days.[123][124]?In an article for?Wired?magazine in September 2014, Cade Metz described deep learning as: "Deep learning tries to mimic the behavior of neural networks in the human brain. In essence, it creates multi-layered software systems that—if properly configured—can train themselves as they analyze more and more data. Whereas traditional machine learning requires an awful lot of hand-holding from human engineers, deep learning does not."[125]Integrating the approaches[ HYPERLINK "" \o "Edit section: Integrating the approaches" edit]Intelligent agent paradigmAn?intelligent agent?is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as?firms). The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works – some agents are symbolic and logical, some are sub-symbolic?neural networks?and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as?decision theory?and economics—that also use concepts of abstract agents. The intelligent agent paradigm became widely accepted during the 1990s.[126]Agent architectures?and?cognitive architecturesResearchers have designed systems to build intelligent systems out of interacting?intelligent agents?in a?multi-agent system.[127]?A system with both symbolic and sub-symbolic components is a?hybrid intelligent system, and the study of such systems is?artificial intelligence systems integration. A?hierarchical control system?provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling.[128]?Rodney Brooks'?subsumption architecture?was an early proposal for such a hierarchical system.[129]Tools[ HYPERLINK "" \o "Edit section: Tools" edit]In the course of 50 years of research, AI has developed a large number of tools to solve the most difficult problems incomputer science. A few of the most general of these methods are discussed below.Search and optimization[ HYPERLINK "" \o "Edit section: Search and optimization" edit]Main articles:?Search algorithm,?Mathematical optimization?and?Evolutionary computationMany problems in AI can be solved in theory by intelligently searching through many possible solutions: HYPERLINK "" \l "cite_note-Search-130" [130]?Reasoning?can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads frompremises?to?conclusions, where each step is the application of an?inference rule.[131]?Planning?algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called?means-ends analysis.[132]?Roboticsalgorithms for moving limbs and grasping objects use?local searches?in?configuration space.[77]?Many?learning?algorithms use search algorithms based on?optimization.Simple exhaustive searches HYPERLINK "" \l "cite_note-Uninformed_search-133" [133]?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 eliminate choices that are unlikely to lead to the goal (called "pruning?the?search tree").?Heuristics?supply the program with a "best guess" for the path on which the solution lies.[134]Heuristics limit the search for solutions into a smaller sample size.[78]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.[135]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). Forms of?evolutionary computation?include?swarm intelligence?algorithms (such as?ant colony?or?particle swarm optimization)[136]?and?evolutionary algorithms?(such as?genetic algorithms,?gene expression programming, andgenetic programming).[137]Logic[ HYPERLINK "" \o "Edit section: Logic" edit]Main articles:?Logic programming?and?Automated reasoningLogic[138]?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[139]?and?inductive logic programming?is a method for?learning.[140]Several different forms of logic are used in AI research.?Propositional?or?sentential logic[141]?is the logic of statements which can be true or false.?First-order logic[142]?also allows the use of?quantifiers?and?predicates, and can express facts about objects, their properties, and their relations with each other.?Fuzzy logic, HYPERLINK "" \l "cite_note-Fuzzy_logic-143" [143]?is a version of first-order logic which allows the truth of a statement to be represented as a value between 0 and 1, rather than simply True (1) or False (0).?Fuzzy systemscan be used for uncertain reasoning and have been widely used in modern industrial and consumer?product control systems.?Subjective logic[144]?models uncertainty in a different and more explicit manner than fuzzy-logic: a given binomial opinion satisfies belief + disbelief + uncertainty = 1 within a?Beta distribution. By this method, ignorance can be distinguished from probabilistic statements that an agent makes with high confidence.Default logics,?non-monotonic logics?and?circumscription[51]?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;[45]?situation calculus,?event calculus?and?fluent calculus?(for representing events and time);[46]?causal calculus;[47]?belief calculus; and?modal logics.[48]Probabilistic methods for uncertain reasoning[ HYPERLINK "" \o "Edit section: Probabilistic methods for uncertain reasoning" edit]Main articles:?Bayesian network,?Hidden Markov model,?Kalman filter,?Decision theory?and?Utility theoryMany 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.[145]Bayesian networks[146]?are a very general tool that can be used for a large number of problems: reasoning (using theBayesian inference?algorithm),[147]?learning?(using the?expectation-maximization algorithm),[148]?planning?(using?decision networks)[149]?and?perception?(using?dynamic Bayesian networks).[150]?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).[150]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,[151]?and?information value theory.[57]?These tools include models such as?Markov decision processes,[152]?dynamic?decision networks,[150]?game theory?and?mechanism design.[153]Classifiers and statistical learning methods[ HYPERLINK "" \o "Edit section: Classifiers and statistical learning methods" edit]Main articles:?Classifier (mathematics),?Statistical classification?and?Machine learningThe 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.[154]A classifier can be trained in various ways; there are many statistical and?machine learning?approaches. The most widely used classifiers are the?neural network,[155]?kernel methods?such as the?support vector machine,[156]?k-nearest neighbor algorithm,[157]?Gaussian mixture model,[158]?naive Bayes classifier,[159]?and?decision tree.[160]?The performance of these classifiers have been compared over a wide range of tasks. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Determining a suitable classifier for a given problem is still more an art than science.[161]Neural networks[ HYPERLINK "" \o "Edit section: Neural networks" edit]Main articles:?Neural network?and?ConnectionismA neural network is an interconnected group of nodes, akin to the vast network of?neurons?in thehuman brain.The study of?artificial neural networks[155]?began in the decade before the field AI research was founded, in the work of?Walter Pitts?and?Warren McCullough. Other important early researchers were?Frank Rosenblatt, who invented the?perceptronand?Paul Werbos?who developed the?backpropagation?algorithm.[162]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). Among the most popular feedforward networks are?perceptrons,?multi-layer perceptrons?and?radial basis networks.[163]?Among recurrent networks, the most famous is the?Hopfield net, a form of attractor network, which was first described by?John Hopfield?in 1982.[164]?Neural networks can be applied to the problem of?intelligent control?(for robotics) or?learning, using such techniques asHebbian learning?and?competitive learning.[165]Hierarchical temporal memory?is an approach that models some of the structural and algorithmic properties of the?neocortex.[166]Control theory[ HYPERLINK "" \o "Edit section: Control theory" edit]Main article:?Intelligent controlControl theory, the grandchild of?cybernetics, has many important applications, especially in?robotics.[167]Languages[ HYPERLINK "" \o "Edit section: Languages" edit]Main article:?List of programming languages for artificial intelligenceAI researchers have developed several specialized languages for AI research, including?Lisp[168]?and?Prolog.[169]Evaluating progress[ HYPERLINK "" \o "Edit section: Evaluating progress" edit]Main article:?Progress in artificial intelligenceIn 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the?Turing test. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.[170]Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed?subject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.[171]One classification for outcomes of an AI test is: HYPERLINK "" \l "cite_note-172" [172]Optimal: it is not possible to perform better.Strong super-human: performs better than all humans.Super-human: performs better than most humans.Sub-human: performs worse than most humans.For example, performance at?draughts?(i.e. checkers) is optimal,[173]?performance at chess is super-human and nearing strong super-human (see?computer chess:?computers versus human) and performance at many everyday tasks (such as recognizing a face or crossing a room without bumping into something) is sub-human.A quite different approach measures machine intelligence through tests which are developed from?mathematical?definitions of intelligence. Examples of these kinds of tests start in the late nineties devising intelligence tests using notions fromKolmogorov complexity?and?data compression.[174]?Two major advantages of mathematical definitions are their applicability to nonhuman intelligences and their absence of a requirement for human testers.A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (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 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.[175]Applications[ HYPERLINK "" \o "Edit section: Applications" edit]An?automated online assistantproviding customer service on a web page – one of many very primitive applications of artificial intelligence.This section requires?expansion.(January 2011)Main article:?Applications of artificial intelligenceArtificial intelligence techniques are pervasive and are too numerous to list. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the?AI effect.[176]?An area that artificial intelligence has contributed greatly to is?intrusion detection.[177]Competitions and prizes[ HYPERLINK "" \o "Edit section: Competitions and prizes" edit]Main article:?Competitions and prizes in artificial intelligenceThere are a number of competitions and prizes to promote research in artificial intelligence. The main areas promoted are: general machine intelligence, conversational behavior, data-mining,?robotic cars, robot soccer and games.Platforms[ HYPERLINK "" \o "Edit section: Platforms" edit]A?platform?(or "computing platform") is defined as "some sort of hardware architecture or software framework (including application frameworks), that allows software to run." As Rodney Brooks[178]?pointed out many years ago, it is not just the artificial intelligence software that defines the AI features of the platform, but rather the actual platform itself that affects the AI that results, i.e., there needs to be work in AI problems on real-world platforms rather than in isolation.A wide variety of platforms has allowed different aspects of AI to develop, ranging from?expert systems, albeit?PC-based but still an entire real-world system, to various robot platforms such as the widely available?Roomba?with open interface.[179]Philosophy[ HYPERLINK "" \o "Edit section: Philosophy" edit]Main article:?Philosophy of artificial intelligenceArtificial intelligence, by claiming to be able to recreate the capabilities of the human?mind, is both a challenge and an inspiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence? Can a machine have a?mind?and?consciousness? A few of the most influential answers to these questions are given below.[180]Turing's "polite convention"We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the?Turing test.[170]The?Dartmouth proposal"Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for the?Dartmouth Conference?of 1956, and represents the position of most working AI researchers.[181]Newell and Simon's physical symbol system hypothesis"A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligences consist of formal operations on symbols.[182]?Hubert Dreyfus?argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See?Dreyfus' critique of AI.)[183][184]G?del's first incompleteness theoremJohn Lucas?(in 1961) and?Roger Penrose?(in 1989) both argued that G?del's theorem entails that artificial intelligence can never surpass human intelligence,[185]?because it shows there are?propositions?which a human being can prove but which can not be proved by a?formal system. A system with a certain amount of arithmetic, cannot prove all true statements, as is possible in formal logic. Formal deductive logic is complete, but when a certain level of number theory is added, the total system becomes incomplete. This is true for a human thinker using these systems, or a computer program. (See Logical Options: An Introduction to Classical and Alternative Logics, Bell et alia, Broadview Press, 2001, pp. 164-65.) Computer languages take on the completeness/incompleteness of the reasoning system that they are used to represent. Although completeness/incompleteness is a very important characteristic of a reasoning system, incomplete systems can be very useful (such as modern mathematics), and the emulation of incomplete reasoning systems in computer code is a core part of artificial intelligence.Searle's strong AI hypothesis"The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."[186]?John Searle counters this assertion with his?Chinese room?argument, which asks us to look?inside?the computer and try to find where the "mind" might be.[187]The?artificial brain?argumentThe brain can be simulated.?Hans Moravec,?Ray Kurzweil?and others have argued that it is technologically feasible to copy the brain directly into hardware and software, and that such a simulation will be essentially identical to the original.[90]Predictions and ethics[ HYPERLINK "" \o "Edit section: Predictions and ethics" edit]Main articles:?Ethics of artificial intelligence,?Transhumanism?and?Technological singularityMany thinkers have speculated about the future of artificial intelligence technology and society. The existence of an artificial intelligence that rivals or exceeds human intelligence raises difficult ethical issues, and the potential power of the technology inspires both hopes and fears.If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to?recursive self-improvement. The new intelligence could thus increase exponentially and dramatically surpass humans.[188]Hyper-intelligent software may not necessarily decide to support the continued existence of mankind, and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth.One proposal to deal with this is to ensure that the first generally intelligent AI is 'Friendly AI', and will then be able to control subsequently developed AIs. Some question whether this kind of check could really remain in place.Martin Ford, author of?The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future, HYPERLINK "" \l "cite_note-Ford2009Lights-189" [189]and others argue that specialized artificial intelligence applications, robotics and other forms of automation will ultimately result in significant unemployment as machines begin to match and exceed the capability of workers to perform most routine and repetitive jobs. Ford predicts that many knowledge-based occupations—and in particular entry level jobs—will be increasingly susceptible to automation via expert systems, machine learning HYPERLINK "" \l "cite_note-190" [190]?and other AI-enhanced applications. AI-based applications may also be used to amplify the capabilities of low-wage offshore workers, making it more feasible tooutsource?knowledge work.[191]Joseph Weizenbaum?wrote that AI applications can not, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as?customer service?or?psychotherapy[192]?was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as?computationalism). To Weizenbaum these points suggest that AI research devalues human life.[193]Many futurists believe that artificial intelligence will ultimately transcend the limits of progress.?Ray Kurzweil?has usedMoore's law?(which describes the relentless exponential improvement in digital technology) to calculate that?desktop computers?will have the same processing power as human brains by the year 2029. He also predicts that by 2045 artificial intelligence will reach a point where it is able to improve?itself?at a rate that far exceeds anything conceivable in the past, a scenario that science fiction writer?Vernor Vinge?named the "singularity".[194]Robot designer?Hans Moravec, cyberneticist?Kevin Warwick?and inventor?Ray Kurzweil?have predicted that humans and machines will merge in the future into?cyborgs?that are more capable and powerful than either.[195]?This idea, calledtranshumanism, which has roots in?Aldous Huxley?and?Robert Ettinger, has been illustrated in fiction as well, for example in the?manga?Ghost in the Shell?and the science-fiction series?Dune. In the 1980s artist?Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including?George Lucas?and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form. Almost 20 years later, the first AI robotic pet,?AIBO, came available as a companion to people. AIBO grew out of Sony's Computer Science Laboratory (CSL). Famed engineer Toshitada Doi is credited as AIBO's original progenitor: in 1994 he had started work on robots with artificial intelligence expert Masahiro Fujita, at CSL. Doi's, friend, the artist Hajime Sorayama, was enlisted to create the initial designs for the AIBO's body. Those designs are now part of the permanent collections of Museum of Modern Art and the Smithsonian Institution, with later versions of AIBO being used in studies in Carnegie Mellon University. In 2006, AIBO was added into Carnegie Mellon University's "Robot Hall of Fame".Political scientist?Charles T. Rubin?believes that AI can be neither designed nor guaranteed to be benevolent.[196]?He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably, because there is no?a priori?reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share).Edward Fredkin?argues that "artificial intelligence is the next stage in evolution", an idea first proposed by?Samuel Butler's "Darwin among the Machines" (1863), and expanded upon by?George Dyson?in his book of the same name in 1998.[197]In fiction[ HYPERLINK "" \o "Edit section: In fiction" edit]Main article:?Artificial intelligence in fictionThe implications of artificial intelligence have also been explored in fiction. Artificial Intelligences have appeared in many roles, including:Jarvis a personal assistant in Iron Man movies.a supercomputer named "ARIIA" in the movie "Eagle_Eye"a supercomputer named "VIKI" and a robot named "Sonny" in the movie "I,Robot"a robot boy named?David?in the movie "A.I.?(and there were other robots that had Artificial intelligence in this movie.)"a real time battlefield analyst (Cortana?in?Halo: Combat Evolved,?Halo 2,?Halo 3, and?Halo 4)a servant (R2-D2?and?C-3PO?in?Star Wars)a law enforcer (K.I.T.T.?"Knight Rider")a comrade (Lt. Commander Data?in?Star Trek: The Next Generation)a conqueror/overlord (The Matrix,?Omnius)a dictator (With Folded Hands),(Colossus: The Forbin Project?(1970 Movie)).a benevolent provider/de facto ruler (The Culture)a supercomputer (The Red Queen?in?Resident Evil?/ "Gilium" in?Outlaw Star?/?Golem XIV)an assassin (Terminator)a sentient race (Battlestar Galactica/Transformers/Mass Effect)an extension to human abilities (Ghost in the Shell)the savior of the human race (R. Daneel Olivaw?in?Isaac Asimov's?Robot?series)the human race critic and philosopher (Golem XIV)a lover (e.g.?Her (film))a host to real (usually deceased) human intelligence (e.g.?Transcendence (2014 film))a supercomputer that detects planned terrorist attacks (Person of Interest)a spaceship vehicle that transports David through time (Flight of the Navigator)Mary Shelley's?Frankenstein?considers a key issue in the?ethics of artificial intelligence: if a machine can be created that has intelligence, could it also?feel? If it can feel, does it have the same rights as a human? The idea also appears in modern science fiction, including the films?I Robot,?Blade Runner,?The Machine?and?A.I.: Artificial Intelligence, in which humanoid machines have the ability to feel human emotions. This issue, now known as "robot rights", is currently being considered by, for example, California's?Institute for the Future, although many critics believe that the discussion is premature.[198]?The subject is profoundly discussed in the 2010 documentary film?Plug & Pray.[199]See also[ HYPERLINK "" \o "Edit section: See also" edit]AI portalMind and Brain portalChess portalStrategy games portalRobotics portalMain article:?Outline of artificial intelligenceArtificial Intelligence?(journal)Artificial intelligence (video games)Computer GoHuman Cognome ProjectList of artificial intelligence projectsList of artificial intelligence researchersList of emerging technologiesList of important artificial intelligence publicationsList of machine learning algorithmsList of scientific journalsNever-Ending Language LearningOur Final InventionPhilosophy of mindSimulated realityReferences[ HYPERLINK "" \o "Edit section: References" edit]Notes[ HYPERLINK "" \o "Edit section: Notes" edit]Jump up^?Although there is some controversy on this point (seeCrevier (1993, p.?50)),?McCarthy?states unequivocally "I came up with the term" in a c|net interview. (Skillings 2006) McCarthy first used the term in the proposal for theDartmouth conference, which appeared in 1955. (McCarthy et al. 1955)Jump up^?McCarthy's definition of AI:McCarthy 2007Jump up^?Pamela?McCorduck (2004, pp.?424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics?... and these with own sub-subfield—that would hardly have anything to say to each other."^?Jump up to:a?b?This list of intelligent traits is based on the topics covered by the major AI textbooks, including:Russell & Norvig 2003Luger & Stubblefield 2004Poole, Mackworth & Goebel 1998Nilsson 1998^?Jump up to:a?b?General intelligence (strong AI) is discussed in popular introductions to AI:Kurzweil 1999?and?Kurzweil 2005Jump up^?See the?Dartmouth proposal, under?Philosophy, below.^?Jump up to:a?b?This is a central idea of?Pamela McCorduck'sMachines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition." (McCorduck 2004, p.?34) "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized." (McCorduck 2004, p.?xviii) "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction." (McCorduck 2004, p.?3) She traces the desire back to its?Hellenistic?roots and calls it the urge to "forge the Gods." (McCorduck 2004, pp.?340–400)Jump up^?The optimism referred to includes the predictions of early AI researchers (see?optimism in the history of AI) as well as the ideas of modern?transhumanists?such as?Ray Kurzweil.Jump up^?The "setbacks" referred to include the?ALPAC report?of 1966, the abandonment of?perceptrons?in 1970,?the Lighthill Report?of 1973 and the?collapse of the Lisp machine marketin 1987.^?Jump up to:a?b?AI applications widely used behind the scenes:Russell & Norvig 2003, p.?28Kurzweil 2005, p.?265NRC 1999, pp.?216–222Jump up^?Eden, Amnon; Moor, James; S?raker, Johnny; Steinhart, Eric, eds. (2013).?Singularity Hypotheses: A Scientific and Philosophical Assessment. Springer. p.?1.Jump up^?Carvalko, Joseph (2012).?The Techno-human Shell-A Jump in the Evolutionary Gap. Sunbury Press.?ISBN?978-1620061657.Jump up^?AI in myth:McCorduck 2004, pp.?4–5Russell & Norvig 2003, p.?939Jump up^?Cult images?as artificial intelligence:Crevier (1993, p.?1) (statue of?Amun)McCorduck (2004, pp.?6–9)These were the first machines to be believed to have true intelligence and consciousness.?Hermes Trismegistusexpressed the common belief that with these statues, craftsman had reproduced "the true nature of the gods", their?sensus?and?spiritus. McCorduck makes the connection between sacred automatons and?Mosaic law(developed around the same time), which expressly forbids the worship of robots (McCorduck 2004, pp.?6–9)Jump up^?Humanoid automata:Yan Shi:Needham 1986, p.?53Hero of Alexandria:McCorduck 2004, p.?6Al-Jazari:"A Thirteenth Century Programmable Robot". Shef.ac.uk. Retrieved 25 April 2009.Wolfgang von Kempelen:McCorduck 2004, p.?17Jump up^?Artificial beings:Jābir ibn Hayyān's?Takwin:O'Connor, Kathleen Malone (1994).?"The alchemical creation of life (takwin) and other concepts of Genesis in medieval Islam". University of Pennsylvania. Retrieved 10 January 2007.Judah Loew's?Golem:McCorduck 2004, pp.?15–16Buchanan 2005, p.?50Paracelsus' Homunculus:McCorduck 2004, pp.?13–14Jump up^?AI in early science fiction.McCorduck 2004, pp.?17–25Jump up^?This insight, that digital computers can simulate any process of formal reasoning, is known as the?Church–Turing thesis.Jump up^?Formal reasoning:Berlinski, David?(2000).?The Advent of the Algorithm. Harcourt Books.?ISBN?0-15-601391-6.OCLC?46890682.^?Jump up to:a?b?AI's immediate precursors:McCorduck 2004, pp.?51–107Crevier 1993, pp.?27–32Russell & Norvig 2003, pp.?15, 940Moravec 1988, p.?3See also?Cybernetics and early neural networks?(in?History of artificial intelligence). Among the researchers who laid the foundations of AI were?Alan Turing,?John von Neumann,Norbert Wiener,?Claude Shannon,?Warren McCullough,Walter Pitts?and?Donald Hebb.Jump up^?Dartmouth conference:McCorduck 2004, pp.?111–136Crevier 1993, pp.?47–49, who writes "the conference is generally recognized as the official birthdate of the new science."Russell & Norvig 2003, p.?17, who call the conference "the birth of artificial intelligence."NRC 1999, pp.?200–201Jump up^?Hegemony of the Dartmouth conference attendees:Russell & Norvig 2003, p.?17, who write "for the next 20 years the field would be dominated by these people and their students."McCorduck 2004, pp.?129–130Jump up^?Russell and Norvig write "it was astonishing whenever a computer did anything kind of smartish."?Russell & Norvig 2003, p.?18Jump up^?"Golden years" of AI (successful symbolic reasoning programs 1956–1973):McCorduck 2004, pp.?243–252Crevier 1993, pp.?52–107Moravec 1988, p.?9Russell & Norvig 2003, pp.?18–21The programs described are?Daniel Bobrow's?STUDENT,Newell?and?Simon's?Logic Theorist?and?Terry Winograd'sSHRDLU.Jump up^?DARPA?pours money into undirected pure research into AI during the 1960s:McCorduck 2004, pp.?131Crevier 1993, pp.?51, 64–65NRC 1999, pp.?204–205Jump up^?AI in England:Howe 1994Jump up^?Optimism of early AI:Herbert Simon?quote:?Simon 1965, p.?96 quoted inCrevier 1993, p.?109.Marvin Minsky?quote:?Minsky 1967, p.?2 quoted inCrevier 1993, p.?109.Jump up^?See?The problems?(in?History of artificial intelligence)Jump up^?First?AI Winter,?Mansfield Amendment,?Lighthill reportCrevier 1993, pp.?115–117Russell & Norvig 2003, p.?22NRC 1999, pp.?212–213Howe 1994^?Jump up to:a?b?Expert systems:ACM 1998, I.2.1,Russell & Norvig 2003, pp.?22–24Luger & Stubblefield 2004, pp.?227–331,Nilsson 1998, chpt. 17.4McCorduck 2004, pp.?327–335, 434–435Crevier 1993, pp.?145–62, 197–203Jump up^?Boom of the 1980s: rise of?expert systems,?Fifth Generation Project,?Alvey,?MCC,?SCI:McCorduck 2004, pp.?426–441Crevier 1993, pp.?161–162,197–203, 211, 240Russell & Norvig 2003, p.?24NRC 1999, pp.?210–211Jump up^?Second?AI winter:McCorduck 2004, pp.?430–435Crevier 1993, pp.?209–210NRC 1999, pp.?214–216^?Jump up to:a?b?Formal methods are now preferred ("Victory of theneats"):Russell & Norvig 2003, pp.?25–26McCorduck 2004, pp.?486–487Jump up^?McCorduck 2004, pp.?480–483Jump up^?DARPA Grand Challenge – home pageJump up^?"Welcome". Archive.darpa.mil. Retrieved 31 October 2011.Jump up^?Markoff, John (16 February 2011).?"On 'Jeopardy!' Watson Win Is All but Trivial".?The New York Times.Jump up^?Kinect's AI breakthrough explainedJump up^?Problem solving, puzzle solving, game playing and deduction:Russell & Norvig 2003, chpt. 3–9,Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9,Luger & Stubblefield 2004, chpt. 3,4,6,8,Nilsson 1998, chpt. 7–12Jump up^?Uncertain reasoning:Russell & Norvig 2003, pp.?452–644,Poole, Mackworth & Goebel 1998, pp.?345–395,Luger & Stubblefield 2004, pp.?333–381,Nilsson 1998, chpt. 19Jump up^?Intractability and efficiency?and the?combinatorial explosion:Russell & Norvig 2003, pp.?9, 21–22Jump up^?Psychological evidence of sub-symbolic reasoning:Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive?social intelligence, performance dramatically improves. (See?Wason selection task)Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See?list of cognitive biases?for several examples).Lakoff & Nú?ez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See?Where Mathematics Comes From)Jump up^?Knowledge representation:ACM 1998, I.2.4,Russell & Norvig 2003, pp.?320–363,Poole, Mackworth & Goebel 1998, pp.?23–46, 69–81, 169–196, 235–277, 281–298, 319–345,Luger & Stubblefield 2004, pp.?227–243,Nilsson 1998, chpt. 18Jump up^?Knowledge engineering:Russell & Norvig 2003, pp.?260–266,Poole, Mackworth & Goebel 1998, pp.?199–233,Nilsson 1998, chpt. ~17.1–17.4^?Jump up to:a?b?Representing categories and relations:?Semantic networks,?description logics,?inheritance?(including?framesand?scripts):Russell & Norvig 2003, pp.?349–354,Poole, Mackworth & Goebel 1998, pp.?174–177,Luger & Stubblefield 2004, pp.?248–258,Nilsson 1998, chpt. 18.3^?Jump up to:a?b?Representing events and time:Situation calculus,event calculus,?fluent calculus?(including solving the?frame problem):Russell & Norvig 2003, pp.?328–341,Poole, Mackworth & Goebel 1998, pp.?281–298,Nilsson 1998, chpt. 18.2^?Jump up to:a?b?Causal calculus:Poole, Mackworth & Goebel 1998, pp.?335–337^?Jump up to:a?b?Representing knowledge about knowledge:?Belief calculus,?modal logics:Russell & Norvig 2003, pp.?341–344,Poole, Mackworth & Goebel 1998, pp.?275–277Jump up^?Ontology:Russell & Norvig 2003, pp.?320–328Jump up^?Qualification problem:McCarthy & Hayes 1969Russell & Norvig 2003[page?needed]While McCarthy was primarily concerned with issues in the logical representation of actions,?Russell & Norvig 2003apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge.^?Jump up to:a?b?Default reasoning and?default logic,?non-monotonic logics,?circumscription,?closed world assumption,abduction?(Poole?et al.?places abduction under "default reasoning". Luger?et al.?places this under "uncertain reasoning"):Russell & Norvig 2003, pp.?354–360,Poole, Mackworth & Goebel 1998, pp.?248–256, 323–335,Luger & Stubblefield 2004, pp.?335–363,Nilsson 1998, ~18.3.3Jump up^?Breadth of commonsense knowledge:Russell & Norvig 2003, p.?21,Crevier 1993, pp.?113–114,Moravec 1988, p.?13,Lenat & Guha 1989?(Introduction)Jump up^?Dreyfus & Dreyfus 1986Jump up^?Gladwell 2005^?Jump up to:a?b?Expert knowledge as?embodied?intuition:Dreyfus & Dreyfus 1986?(Hubert Dreyfus?is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See?Dreyfus' critique of AI)Gladwell 2005?(Gladwell's?Blink?is a popular introduction to sub-symbolic reasoning and knowledge.)Hawkins & Blakeslee 2005?(Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.)Note, however, that recent work in cognitive science challenges the view that there is anything like sub-symbolic human information processing, i.e., human cognition is essentially symbolic regardless of the level and of the consciousness status of the processing:Augusto, Luis M. (2013). "Unconscious representations 1: Belying the traditional model of human cognition".Axiomathes.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1007/s10516-012-9206-z.Augusto, Luis M. (2013). "Unconscious representations 2: Towards an integrated cognitive architecture".Axiomathes.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1007/s10516-012-9207-y. HYPERLINK "" \l "cite_ref-Planning_56-0" Jump up^? HYPERLINK "" \o "Automated planning and scheduling" Planning: HYPERLINK "" \l "CITEREFACM1998" ACM 1998, ~I.2.8, HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?375–459, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?281–316, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?314–329, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 10.1–2, 22^? HYPERLINK "" \l "cite_ref-Information_value_theory_57-0" Jump up to:a? HYPERLINK "" \l "cite_ref-Information_value_theory_57-1" b? HYPERLINK "" \o "Applied information economics" Information value theory: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?600–604 HYPERLINK "" \l "cite_ref-Classical_planning_58-0" Jump up^?Classical planning: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?375–430, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?281–315, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?314–329, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 10.1–2, 22 HYPERLINK "" \l "cite_ref-Non-deterministic_planning_59-0" Jump up^?Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?430–449 HYPERLINK "" \l "cite_ref-Multi-agent_planning_60-0" Jump up^?Multi-agent planning and emergent behavior: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?449–455 HYPERLINK "" \l "cite_ref-61" Jump up^?This is a form of? HYPERLINK "" \o "Tom M. Mitchell" Tom Mitchell's widely quoted definition of machine learning: "A computer program is set to learn from an experience?E?with respect to some task?T?and some performance measure?P?if its performance on?T?as measured by?P?improves with experience?E." HYPERLINK "" \l "cite_ref-Machine_learning_62-0" Jump up^? HYPERLINK "" \o "Machine learning" Learning: HYPERLINK "" \l "CITEREFACM1998" ACM 1998, I.2.6, HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?649–788, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?397–438, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?385–542, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 3.3, 10.3, 17.5, 20 HYPERLINK "" \l "cite_ref-63" Jump up^? HYPERLINK "" \o "Alan Turing" Alan Turing?discussed the centrality of learning as early as 1950, in his classic paper " HYPERLINK "" \o "Computing Machinery and Intelligence" Computing Machinery and Intelligence".( HYPERLINK "" \l "CITEREFTuring1950" Turing 1950) In 1956, at the original Dartmouth AI summer conference,? HYPERLINK "" \o "Ray Solomonoff" Ray Solomonoff?wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine". HYPERLINK "" (pdf scanned copy of the original)?(version published in 1957, An Inductive Inference Machine," IRE Convention Record, Section on Information Theory, Part 2, pp. 56–62) HYPERLINK "" \l "cite_ref-Reinforcement_learning_64-0" Jump up^? HYPERLINK "" \o "Reinforcement learning" Reinforcement learning: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?763–788 HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?442–449 HYPERLINK "" \l "cite_ref-Computational_learning_theory_65-0" Jump up^? HYPERLINK "" \o "Computational learning theory" Computational learning theory:CITATION IN PROGRESS.[ HYPERLINK "" \o "Wikipedia:Citation needed" citation needed] HYPERLINK "" \l "cite_ref-Weng01_66-0" Jump up^?Weng, J., McClelland, Pentland, A.,Sporns, O., Stockman, I., Sur, M., and E. Thelen (2001)? HYPERLINK "" "Autonomous mental development by robots and animals",?Science, vol. 291, pp. 599–600. HYPERLINK "" \l "cite_ref-Lungarella03_67-0" Jump up^?Lungarella, M.; Metta, G.; Pfeifer, R.; Sandini, G. (2003). "Developmental robotics: a survey".?Connection Science15: 151–190.? HYPERLINK "" \l "CiteSeerX" \o "CiteSeer" CiteSeerX:? HYPERLINK "" 10.1.1.83.7615. HYPERLINK "" \l "cite_ref-Asada09_68-0" Jump up^?Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino, M. and C. Yoshida (2009) HYPERLINK "" "Cognitive developmental robotics: a survey".?IEEE Transactions on Autonomous Mental Development, Vol.1, No.1, pp.12--34. HYPERLINK "" \l "cite_ref-Oudeyer10_69-0" Jump up^?Oudeyer, P-Y. (2010)? HYPERLINK "" "On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive development",?IEEE Transactions on Autonomous Mental Development, 2(1), pp. 2--16. HYPERLINK "" \l "cite_ref-Natural_language_processing_70-0" Jump up^? HYPERLINK "" \o "Natural language processing" Natural language processing: HYPERLINK "" \l "CITEREFACM1998" ACM 1998, I.2.7 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?790–831 HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?91–104 HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?591–632 HYPERLINK "" \l "cite_ref-Applications_of_natural_language_processing_71-0" Jump up^?Applications of natural language processing, including HYPERLINK "" \o "Information retrieval" information retrieval?(i.e.? HYPERLINK "" \o "Text mining" text mining) and? HYPERLINK "" \o "Machine translation" machine translation: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?840–857, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?623–630 HYPERLINK "" \l "cite_ref-Machine_perception_72-0" Jump up^? HYPERLINK "" \o "Machine perception" Machine perception: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?537–581, 863–898 HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, ~chpt. 6 HYPERLINK "" \l "cite_ref-Computer_vision_73-0" Jump up^? HYPERLINK "" \o "Computer vision" Computer vision: HYPERLINK "" \l "CITEREFACM1998" ACM 1998, I.2.10 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?863–898 HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 6 HYPERLINK "" \l "cite_ref-Speech_recognition_74-0" Jump up^? HYPERLINK "" \o "Speech recognition" Speech recognition: HYPERLINK "" \l "CITEREFACM1998" ACM 1998, ~I.2.7 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?568–578 HYPERLINK "" \l "cite_ref-Object_recognition_75-0" Jump up^? HYPERLINK "" \o "Object recognition" Object recognition: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?885–892 HYPERLINK "" \l "cite_ref-Robotics_76-0" Jump up^? HYPERLINK "" \o "Robotic" Robotics: HYPERLINK "" \l "CITEREFACM1998" ACM 1998, I.2.9, HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?901–942, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?443–460^? HYPERLINK "" \l "cite_ref-Configuration_space_77-0" Jump up to:a? HYPERLINK "" \l "cite_ref-Configuration_space_77-1" b?Moving and? HYPERLINK "" \o "Configuration space" configuration space: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?916–932^? HYPERLINK "" \l "cite_ref-Tecuci.2C_G._2012_78-0" Jump up to:a? HYPERLINK "" \l "cite_ref-Tecuci.2C_G._2012_78-1" b?Tecuci, G. (2012). "Artificial intelligence".?Wiley Interdisciplinary Reviews: Computational Statistics?4?(2): 168–180.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1002/wics.200.? HYPERLINK "" edit HYPERLINK "" \l "cite_ref-Robotic_mapping_79-0" Jump up^? HYPERLINK "" \o "Robotic mapping" Robotic mapping?(localization, etc): HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?908–915 HYPERLINK "" \l "cite_ref-80" Jump up^? HYPERLINK "" "Kismet". MIT Artificial Intelligence Laboratory, Humanoid Robotics Group. HYPERLINK "" \l "cite_ref-81" Jump up^?Thro, Ellen (1993).?Robotics. New York. HYPERLINK "" \l "cite_ref-82" Jump up^?Edelson, Edward (1991).?The Nervous System. New York: Remmel Nunn. HYPERLINK "" \l "cite_ref-TaoTan_83-0" Jump up^?Tao, Jianhua; Tieniu Tan (2005). "Affective Computing: A Review". "Affective Computing and Intelligent Interaction". HYPERLINK "" \o "LNCS" LNCS?3784. Springer. pp.?981–995. HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1007/11573548. HYPERLINK "" \l "cite_ref-84" Jump up^?James, William (1884). "What is Emotion".?Mind?9: 188–205.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1093/mind/os-IX.34.188.?Cited by Tao and Tan. HYPERLINK "" \l "cite_ref-85" Jump up^? HYPERLINK "" "Affective Computing"?MIT Technical Report #321 ( HYPERLINK "" Abstract), 1995 HYPERLINK "" \l "cite_ref-86" Jump up^?Kleine-Cosack, Christian (October 2006).? HYPERLINK "" "Recognition and Simulation of Emotions"?(PDF). Archived from? HYPERLINK "" the original?on 28 May 2008. Retrieved 13 May 2008. "The introduction of emotion to computer science was done by Pickard (sic) who created the field of affective computing." HYPERLINK "" \l "cite_ref-87" Jump up^?Diamond, David (December 2003).? HYPERLINK "" "The Love Machine; Building computers that care". Wired.? HYPERLINK "" Archived?from the original on 18 May 2008. Retrieved 13 May 2008. "Rosalind Picard, a genial MIT professor, is the field's godmother; her 1997 book, Affective Computing, triggered an explosion of interest in the emotional side of computers and their users." HYPERLINK "" \l "cite_ref-Emotion_and_affective_computing_88-0" Jump up^?Emotion and? HYPERLINK "" \o "Affective computing" affective computing: HYPERLINK "" \l "CITEREFMinsky2006" Minsky 2006 HYPERLINK "" \l "cite_ref-Artificial_consciousness_89-0" Jump up^? HYPERLINK "" \o "Gerald Edelman" Gerald Edelman,? HYPERLINK "" \o "Igor Aleksander" Igor Aleksander?and others have both argued that? HYPERLINK "" \o "Artificial consciousness" artificial consciousness?is required for strong AI. ( HYPERLINK "" \l "CITEREFAleksander1995" Aleksander 1995;? HYPERLINK "" \l "CITEREFEdelman2007" Edelman 2007)^? HYPERLINK "" \l "cite_ref-Brain_simulation_90-0" Jump up to:a? HYPERLINK "" \l "cite_ref-Brain_simulation_90-1" b? HYPERLINK "" \o "Artificial brain" Artificial brain?arguments: AI requires a simulation of the operation of the human brain HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?957 HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?271 and 279A few of the people who make some form of the argument: HYPERLINK "" \l "CITEREFMoravec1988" Moravec 1988 HYPERLINK "" \l "CITEREFKurzweil2005" Kurzweil 2005, p.?262 HYPERLINK "" \l "CITEREFHawkinsBlakeslee2005" Hawkins & Blakeslee 2005The most extreme form of this argument (the brain replacement scenario) was put forward by? HYPERLINK "" \o "Clark Glymour" Clark Glymour?in the mid-1970s and was touched on by? HYPERLINK "" \o "Zenon Pylyshyn" Zenon Pylyshyn?and HYPERLINK "" \o "John Searle" John Searle?in 1980. HYPERLINK "" \l "cite_ref-AI_complete_91-0" Jump up^? HYPERLINK "" \o "AI complete" AI complete:? HYPERLINK "" \l "CITEREFShapiro1992" Shapiro 1992, p.?9 HYPERLINK "" \l "cite_ref-92" Jump up^? HYPERLINK "(researcher)" \o "Nils Nilsson (researcher)" Nils Nilsson?writes: "Simply put, there is wide disagreement in the field about what AI is all about" ( HYPERLINK "" \l "CITEREFNilsson1983" Nilsson 1983, p.?10).^? HYPERLINK "" \l "cite_ref-Biological_intelligence_vs._intelligence_in_general_93-0" Jump up to:a? HYPERLINK "" \l "cite_ref-Biological_intelligence_vs._intelligence_in_general_93-1" b?Biological intelligence vs. intelligence in general: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?2–3, who make the analogy with? HYPERLINK "" \o "Aeronautical engineering" aeronautical engineering. HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplioshed, and the other aimed at modeling intelligent processes found in nature, particularly human ones." HYPERLINK "" \l "CITEREFKolata1982" Kolata 1982, a paper in? HYPERLINK "(journal)" \o "Science (journal)" Science, which describes HYPERLINK "(computer_scientist)" \o "John McCarthy (computer scientist)" McCathy's?indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real" HYPERLINK "" [1]. McCarthy recently reiterated his position at the? HYPERLINK "" \o "AI@50" AI@50?conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence" ( HYPERLINK "" \l "CITEREFMaker2006" Maker 2006).^? HYPERLINK "" \l "cite_ref-Neats_vs._scruffies_94-0" Jump up to:a? HYPERLINK "" \l "cite_ref-Neats_vs._scruffies_94-1" b? HYPERLINK "" \o "Neats vs. scruffies" Neats vs. scruffies: HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?421–424, 486–489 HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?168 HYPERLINK "" \l "CITEREFNilsson1983" Nilsson 1983, pp.?10–11^? HYPERLINK "" \l "cite_ref-Symbolic_vs._sub-symbolic_95-0" Jump up to:a? HYPERLINK "" \l "cite_ref-Symbolic_vs._sub-symbolic_95-1" b?Symbolic vs. sub-symbolic AI: HYPERLINK "" \l "CITEREFNilsson1998" Nilsson (1998, p.?7), who uses the term "sub-symbolic". HYPERLINK "" \l "cite_ref-FOOTNOTEHaugeland1985255_96-0" Jump up^? HYPERLINK "" \l "CITEREFHaugeland1985" Haugeland 1985, p.?255. HYPERLINK "" \l "cite_ref-97" Jump up^? HYPERLINK "" HYPERLINK "" \l "cite_ref-Wang2008_98-0" Jump up^?Pei Wang (2008).? HYPERLINK "" Artificial general intelligence, 2008: proceedings of the First AGI Conference. IOS Press. p.?63.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/978-1-58603-833-5" 978-1-58603-833-5. Retrieved 31 October 2011. HYPERLINK "" \l "cite_ref-GOFAI_99-0" Jump up^? HYPERLINK "" \l "CITEREFHaugeland1985" Haugeland 1985, pp.?112–117 HYPERLINK "" \l "cite_ref-100" Jump up^?The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of HYPERLINK "" \o "Perceptron" perceptrons?by? HYPERLINK "" \o "Marvin Minsky" Marvin Minsky?and? HYPERLINK "" \o "Seymour Papert" Seymour Papert?in 1969. See? HYPERLINK "" \o "History of AI" History of AI,? HYPERLINK "" \o "AI winter" AI winter, or? HYPERLINK "" \o "Frank Rosenblatt" Frank Rosenblatt. HYPERLINK "" \l "cite_ref-AI_at_CMU_in_the_60s_101-0" Jump up^?Cognitive simulation,? HYPERLINK "" \o "Allen Newell" Newell?and? HYPERLINK "" \o "Herbert A. Simon" Simon, AI at? HYPERLINK "" \o "Carnegie Mellon University" CMU?(then called? HYPERLINK "" \o "Carnegie Tech" Carnegie Tech): HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?139–179, 245–250, 322–323 (EPAM) HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?145–149 HYPERLINK "" \l "cite_ref-Soar_102-0" Jump up^? HYPERLINK "(cognitive_architecture)" \o "Soar (cognitive architecture)" Soar?(history): HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?450–451 HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?258–263 HYPERLINK "" \l "cite_ref-AI_at_Stanford_in_the_60s_103-0" Jump up^? HYPERLINK "(computer_scientist)" \o "John McCarthy (computer scientist)" McCarthy?and AI research at? HYPERLINK "" \o "Stanford Artificial Intelligence Laboratory" SAIL?and? HYPERLINK "" \o "SRI International" SRI International: HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?251–259 HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993 HYPERLINK "" \l "cite_ref-AI_at_Edinburgh_and_France_in_the_60s_104-0" Jump up^?AI research at? HYPERLINK "" \o "University of Edinburgh" Edinburgh?and in France, birth of? HYPERLINK "" \o "Prolog" Prolog: HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?193–196 HYPERLINK "" \l "CITEREFHowe1994" Howe 1994 HYPERLINK "" \l "cite_ref-AI_at_MIT_in_the_60s_105-0" Jump up^? HYPERLINK "" \o "AI" AI?at? HYPERLINK "" \o "MIT" MIT?under? HYPERLINK "" \o "Marvin Minsky" Marvin Minsky?in the 1960s?: HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?259–305 HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?83–102, 163–176 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?19 HYPERLINK "" \l "cite_ref-Cyc_106-0" Jump up^? HYPERLINK "" \o "Cyc" Cyc: HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, p.?489, who calls it "a determinedly scruffy enterprise" HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?239–243 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?363?365 HYPERLINK "" \l "CITEREFLenatGuha1989" Lenat & Guha 1989 HYPERLINK "" \l "cite_ref-Knowledge_revolution_107-0" Jump up^?Knowledge revolution: HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?266–276, 298–300, 314, 421 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?22–23 HYPERLINK "" \l "cite_ref-Embodied_AI_108-0" Jump up^? HYPERLINK "" \o "Embodied agent" Embodied?approaches to AI: HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?454–462 HYPERLINK "" \l "CITEREFBrooks1990" Brooks 1990 HYPERLINK "" \l "CITEREFMoravec1988" Moravec 1988 HYPERLINK "" \l "cite_ref-Revival_of_connectionism_109-0" Jump up^?Revival of? HYPERLINK "" \o "Connectionism" connectionism: HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?214–215 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?25 HYPERLINK "" \l "cite_ref-Computational_intelligence_110-0" Jump up^? HYPERLINK "" \o "Computational intelligence" Computational intelligence HYPERLINK "" IEEE Computational Intelligence Society HYPERLINK "" \l "cite_ref-111" Jump up^?Hutter, M. (2012). "One Decade of Universal Artificial Intelligence". "Theoretical Foundations of Artificial General Intelligence". Atlantis Thinking Machines?4. p.?67. HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.2991/978-94-91216-62-6_5.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/978-94-91216-61-9" 978-94-91216-61-9.? HYPERLINK "" edit HYPERLINK "" \l "cite_ref-112" Jump up^?Langley, P. (2011). "The changing science of machine learning".?Machine Learning?82?(3): 275–279. HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1007/s10994-011-5242-y.? HYPERLINK "" edit HYPERLINK "" \l "cite_ref-113" Jump up^?Yarden Katz,? HYPERLINK "" "Noam Chomsky on Where Artificial Intelligence Went Wrong", The Atlantic, November 1, 2012 HYPERLINK "" \l "cite_ref-114" Jump up^?Peter Norvig,? HYPERLINK "" "On Chomsky and the Two Cultures of Statistical Learning" HYPERLINK "" \l "cite_ref-FUKU1980_115-0" Jump up^?K. Fukushima., "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,"?Biol. Cybern., 36, 193–202, 1980 HYPERLINK "" \l "cite_ref-WERBOS1974_116-0" Jump up^?P. Werbos., "Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences,"?PhD thesis, Harvard University, 1974. HYPERLINK "" \l "cite_ref-LECUN1989_117-0" Jump up^?LeCun?et al., "Backpropagation Applied to Handwritten Zip Code Recognition,"?Neural Computation, 1, pp. 541–551, 1989. HYPERLINK "" \l "cite_ref-HOCH1991_118-0" Jump up^?S. Hochreiter., "Untersuchungen zu dynamischen neuronalen Netzen,"?Diploma thesis. Institut f. Informatik, Technische Univ. Munich. Advisor: J. Schmidhuber, 1991. HYPERLINK "" \l "cite_ref-HOCH2001_119-0" Jump up^?S. Hochreiter?et al., "Gradient flow in recurrent nets: the difficulty of learning long-term dependencies,"?In S. C. Kremer and J. F. Kolen, editors, A Field Guide to Dynamical Recurrent Neural Networks. IEEE Press, 2001. HYPERLINK "" \l "cite_ref-HINTON2007_120-0" Jump up^?G. E. Hinton., "Learning multiple layers of representation,"?Trends in Cognitive Sciences, 11, pp. 428–434, 2007. HYPERLINK "" \l "cite_ref-SCHMID1992_121-0" Jump up^?J. Schmidhuber., "Learning complex, extended sequences using the principle of history compression,"Neural Computation, 4, pp. 234–242, 1992. HYPERLINK "" \l "cite_ref-SCHMID1991_122-0" Jump up^?J. Schmidhuber., "My First Deep Learning System of 1991 + Deep Learning Timeline 1962–2013." HYPERLINK "" \l "cite_ref-CIRESAN2010_123-0" Jump up^?D. C. Ciresan?et al., "Deep Big Simple Neural Nets for Handwritten Digit Recognition,"?Neural Computation, 22, pp. 3207–3220, 2010. HYPERLINK "" \l "cite_ref-RAINA2009_124-0" Jump up^?R. Raina, A. Madhavan, A. Ng., "Large-scale Deep Unsupervised Learning using Graphics Processors,"?Proc. 26th Int. Conf. on Machine Learning, 2009. HYPERLINK "" \l "cite_ref-125" Jump up^?Cade Metz. "You Don't Have to Be Google',?Wiredmagazine, 9.26.2014. HYPERLINK "" \l "cite_ref-Intelligent_agents_126-0" Jump up^?The? HYPERLINK "" \o "Intelligent agent" intelligent agent?paradigm: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?27, 32–58, 968–972 HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?7–21 HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?235–240 HYPERLINK "" \l "CITEREFHutter2005" Hutter 2005, pp.?125–126The definition used in this article, in terms of goals, actions, perception and environment, is due to? HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. HYPERLINK "" \l "cite_ref-Agent_architectures_127-0" Jump up^? HYPERLINK "" \o "Agent architecture" Agent architectures,? HYPERLINK "" \o "Hybrid intelligent system" hybrid intelligent systems: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig (2003, pp.?27, 932, 970–972) HYPERLINK "" \l "CITEREFNilsson1998" Nilsson (1998, chpt. 25) HYPERLINK "" \l "cite_ref-Hierarchical_control_system_128-0" Jump up^? HYPERLINK "" \o "Hierarchical control system" Hierarchical control system:Albus, J. S.? HYPERLINK "" 4-D/RCS reference model architecture for unmanned ground vehicles.?In G Gerhart, R Gunderson, and C Shoemaker, editors, Proceedings of the SPIE AeroSense Session on Unmanned Ground Vehicle Technology, volume 3693, pages 11—20 HYPERLINK "" \l "cite_ref-Subsumption_architecture_129-0" Jump up^? HYPERLINK "" \o "Subsumption architecture" Subsumption architecture:CITATION IN PROGRESS.[ HYPERLINK "" \o "Wikipedia:Citation needed" citation needed] HYPERLINK "" \l "cite_ref-Search_130-0" Jump up^? HYPERLINK "" \o "Search algorithm" Search algorithms: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?59–189 HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?113–163 HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?79–164, 193–219 HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 7–12 HYPERLINK "" \l "cite_ref-Logic_as_search_131-0" Jump up^? HYPERLINK "" \o "Forward chaining" Forward chaining,? HYPERLINK "" \o "Backward chaining" backward chaining,? HYPERLINK "" \o "Horn clause" Horn clauses, and logical deduction as search: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?217–225, 280–294 HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?~46–52 HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?62–73 HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 4.2, 7.2 HYPERLINK "" \l "cite_ref-Planning_as_search_132-0" Jump up^? HYPERLINK "" \o "State space search" State space search?and? HYPERLINK "" \o "Automated planning and scheduling" planning: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?382–387 HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?298–305 HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 10.1–2 HYPERLINK "" \l "cite_ref-Uninformed_search_133-0" Jump up^?Uninformed searches ( HYPERLINK "" \o "Breadth first search" breadth first search,? HYPERLINK "" \o "Depth first search" depth first search?and general? HYPERLINK "" \o "State space search" state space search): HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?59–93 HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?113–132 HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?79–121 HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 8 HYPERLINK "" \l "cite_ref-Informed_search_134-0" Jump up^? HYPERLINK "" \o "Heuristic" Heuristic?or informed searches (e.g., greedy? HYPERLINK "" \o "Best-first search" best first?and HYPERLINK "*" \o "A*" A*): HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?94–109, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?pp. 132–147, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?133–150, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 9 HYPERLINK "" \l "cite_ref-Optimization_search_135-0" Jump up^? HYPERLINK "(mathematics)" \o "Optimization (mathematics)" Optimization?searches: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?110–116,120–129 HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?56–163 HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?127–133 HYPERLINK "" \l "cite_ref-Society_based_learning_136-0" Jump up^? HYPERLINK "" \o "Artificial life" Artificial life?and society based learning: HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?530–541 HYPERLINK "" \l "cite_ref-Genetic_programming_137-0" Jump up^? HYPERLINK "" \o "Genetic programming" Genetic programming?and? HYPERLINK "" \o "Genetic algorithms" genetic algorithms: HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?509–530, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 4.2.Holland, John H. (1975).?Adaptation in Natural and Artificial Systems. University of Michigan Press. HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-262-58111-6" 0-262-58111-6.Koza, John R. (1992).?Genetic Programming (On the Programming of Computers by Means of Natural Selection). MIT Press.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-262-11170-5" 0-262-11170-5.Poli, R., Langdon, W. B., McPhee, N. F. (2008).?A Field Guide to Genetic Programming. , freely available from? HYPERLINK "" . HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/978-1-4092-0073-4" 978-1-4092-0073-4. HYPERLINK "" \l "cite_ref-Logic_138-0" Jump up^? HYPERLINK "" \o "Logic" Logic: HYPERLINK "" \l "CITEREFACM1998" ACM 1998, ~I.2.3, HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?194–310, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?35–77, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 13–16 HYPERLINK "" \l "cite_ref-Satplan_139-0" Jump up^? HYPERLINK "" \o "Satplan" Satplan: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?402–407, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?300–301, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 21 HYPERLINK "" \l "cite_ref-Symbolic_learning_techniques_140-0" Jump up^? HYPERLINK "" \o "Explanation based learning" Explanation based learning,? HYPERLINK "" \o "Relevance based learning (page does not exist)" relevance based learning, HYPERLINK "" \o "Inductive logic programming" inductive logic programming,? HYPERLINK "" \o "Case based reasoning" case based reasoning: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?678–710, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?414–416, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?~422–442, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 10.3, 17.5 HYPERLINK "" \l "cite_ref-Propositional_logic_141-0" Jump up^? HYPERLINK "" \o "Propositional logic" Propositional logic: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?204–233, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?45–50 HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 13 HYPERLINK "" \l "cite_ref-First-order_logic_142-0" Jump up^? HYPERLINK "" \o "First-order logic" First-order logic?and features such as? HYPERLINK "(mathematics)" \o "Equality (mathematics)" equality: HYPERLINK "" \l "CITEREFACM1998" ACM 1998, ~I.2.4, HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?240–310, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?268–275, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?50–62, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 15 HYPERLINK "" \l "cite_ref-Fuzzy_logic_143-0" Jump up^? HYPERLINK "" \o "Fuzzy logic" Fuzzy logic: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?526–527 HYPERLINK "" \l "cite_ref-Subjective_logic_144-0" Jump up^? HYPERLINK "" \o "Subjective logic" Subjective logic:CITATION IN PROGRESS.[ HYPERLINK "" \o "Wikipedia:Citation needed" citation needed] HYPERLINK "" \l "cite_ref-Stochastic_methods_for_uncertain_reasoning_145-0" Jump up^?Stochastic methods for uncertain reasoning: HYPERLINK "" \l "CITEREFACM1998" ACM 1998, ~I.2.3, HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?462–644, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?345–395, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?165–191, 333–381, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 19 HYPERLINK "" \l "cite_ref-Bayesian_networks_146-0" Jump up^? HYPERLINK "" \o "Bayesian network" Bayesian networks: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?492–523, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?361–381, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?~182–190, ~363–379, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 19.3–4 HYPERLINK "" \l "cite_ref-Bayesian_inference_147-0" Jump up^? HYPERLINK "" \o "Bayesian inference" Bayesian inference?algorithm: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?504–519, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?361–381, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?~363–379, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 19.4 & 7 HYPERLINK "" \l "cite_ref-Bayesian_learning_148-0" Jump up^? HYPERLINK "" \o "Bayesian learning" Bayesian learning?and the? HYPERLINK "" \o "Expectation-maximization algorithm" expectation-maximization algorithm: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?712–724, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?424–433, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 20 HYPERLINK "" \l "cite_ref-Bayesian_decision_networks_149-0" Jump up^? HYPERLINK "" \o "Bayesian decision theory" Bayesian decision theory?and Bayesian? HYPERLINK "" \o "Decision network" decision networks: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?597–600^? HYPERLINK "" \l "cite_ref-Stochastic_temporal_models_150-0" Jump up to:a? HYPERLINK "" \l "cite_ref-Stochastic_temporal_models_150-1" b? HYPERLINK "" \l "cite_ref-Stochastic_temporal_models_150-2" c?Stochastic temporal models: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?537–581 HYPERLINK "" \o "Dynamic Bayesian network" Dynamic Bayesian networks: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?551–557 HYPERLINK "" \o "Hidden Markov model" Hidden Markov model:( HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?549–551) HYPERLINK "" \o "Kalman filter" Kalman filters: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?551–557 HYPERLINK "" \l "cite_ref-Decisions_theory_and_analysis_151-0" Jump up^? HYPERLINK "" \o "Decision theory" decision theory?and? HYPERLINK "" \o "Decision analysis" decision analysis: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?584–597, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?381–394 HYPERLINK "" \l "cite_ref-Markov_decision_process_152-0" Jump up^? HYPERLINK "" \o "Markov decision process" Markov decision processes?and dynamic? HYPERLINK "" \o "Decision network" decision networks: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?613–631 HYPERLINK "" \l "cite_ref-Game_theory_and_mechanism_design_153-0" Jump up^? HYPERLINK "" \o "Game theory" Game theory?and? HYPERLINK "" \o "Mechanism design" mechanism design: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?631–643 HYPERLINK "" \l "cite_ref-Classifiers_154-0" Jump up^?Statistical learning methods and? HYPERLINK "(mathematics)" \o "Classifier (mathematics)" classifiers: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?712–754, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?453–541^? HYPERLINK "" \l "cite_ref-Neural_networks_155-0" Jump up to:a? HYPERLINK "" \l "cite_ref-Neural_networks_155-1" b?Neural networks and connectionism: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?736–748, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?408–414, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?453–505, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 3 HYPERLINK "" \l "cite_ref-Kernel_methods_156-0" Jump up^? HYPERLINK "" \o "Kernel methods" kernel methods?such as the? HYPERLINK "" \o "Support vector machine" support vector machine, HYPERLINK "" \o "Kernel methods" Kernel methods: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?749–752 HYPERLINK "" \l "cite_ref-K-nearest_neighbor_algorithm_157-0" Jump up^? HYPERLINK "" \o "K-nearest neighbor algorithm" K-nearest neighbor algorithm: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?733–736 HYPERLINK "" \l "cite_ref-Guassian_mixture_model_158-0" Jump up^? HYPERLINK "" \o "Gaussian mixture model" Gaussian mixture model: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?725–727 HYPERLINK "" \l "cite_ref-Naive_Bayes_classifier_159-0" Jump up^? HYPERLINK "" \o "Naive Bayes classifier" Naive Bayes classifier: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?718 HYPERLINK "" \l "cite_ref-Decision_tree_160-0" Jump up^? HYPERLINK "" \o "Alternating decision tree" Decision tree: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?653–664, HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?403–408, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?408–417 HYPERLINK "" \l "cite_ref-Classifier_performance_161-0" Jump up^?Classifier performance: HYPERLINK "" \l "CITEREFvan_der_WaltBernard2006" van der Walt & Bernard 2006 HYPERLINK "" \l "cite_ref-Backpropagation_162-0" Jump up^? HYPERLINK "" \o "Backpropagation" Backpropagation: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?744–748, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?467–474, HYPERLINK "" \l "CITEREFNilsson1998" Nilsson 1998, chpt. 3.3 HYPERLINK "" \l "cite_ref-Feedforward_neural_networks_163-0" Jump up^? HYPERLINK "" \o "Feedforward neural network" Feedforward neural networks,? HYPERLINK "" \o "Perceptron" perceptrons?and? HYPERLINK "" \o "Radial basis network" radial basis networks: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?739–748, 758 HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?458–467 HYPERLINK "" \l "cite_ref-Recurrent_neural_networks_164-0" Jump up^? HYPERLINK "" \o "Recurrent neural networks" Recurrent neural networks,? HYPERLINK "" \o "Hopfield nets" Hopfield nets: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?758 HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?474–505 HYPERLINK "" \l "cite_ref-Learning_in_neural_networks_165-0" Jump up^? HYPERLINK "" \o "Competitive learning" Competitive learning,? HYPERLINK "" \o "Hebbian theory" Hebbian?coincidence learning, HYPERLINK "" \o "Hopfield network" Hopfield networks?and attractor networks: HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?474–505 HYPERLINK "" \l "cite_ref-Hierarchical_temporal_memory_166-0" Jump up^? HYPERLINK "" \o "Hierarchical temporal memory" Hierarchical temporal memory: HYPERLINK "" \l "CITEREFHawkinsBlakeslee2005" Hawkins & Blakeslee 2005 HYPERLINK "" \l "cite_ref-Control_theory_167-0" Jump up^? HYPERLINK "" \o "Control theory" Control theory: HYPERLINK "" \l "CITEREFACM1998" ACM 1998, ~I.2.8, HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?926–932 HYPERLINK "" \l "cite_ref-Lisp_168-0" Jump up^? HYPERLINK "(programming_language)" \o "Lisp (programming language)" Lisp: HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?723–821 HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?59–62, HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?18 HYPERLINK "" \l "cite_ref-Prolog_169-0" Jump up^? HYPERLINK "" \o "Prolog" Prolog: HYPERLINK "" \l "CITEREFPooleMackworthGoebel1998" Poole, Mackworth & Goebel 1998, pp.?477–491, HYPERLINK "" \l "CITEREFLugerStubblefield2004" Luger & Stubblefield 2004, pp.?641–676, 575–581^? HYPERLINK "" \l "cite_ref-Turing_test_170-0" Jump up to:a? HYPERLINK "" \l "cite_ref-Turing_test_170-1" b?The? HYPERLINK "" \o "Turing test" Turing test:Turing's original publication: HYPERLINK "" \l "CITEREFTuring1950" Turing 1950Historical influence and philosophical implications: HYPERLINK "" \l "CITEREFHaugeland1985" Haugeland 1985, pp.?6–9 HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, p.?24 HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?70–71 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?2–3 and 948 HYPERLINK "" \l "cite_ref-Subject_matter_expert_Turing_test_171-0" Jump up^? HYPERLINK "" \o "Subject matter expert Turing test" Subject matter expert Turing test:CITATION IN PROGRESS.[ HYPERLINK "" \o "Wikipedia:Citation needed" citation needed] HYPERLINK "" \l "cite_ref-172" Jump up^?Rajani, Sandeep (2011).? HYPERLINK "" "Artificial Intelligence - Man or Machine".?International Journal of Information Technology and Knowlede Management?4?(1): 173–176. Retrieved 24 September 2012. HYPERLINK "" \l "cite_ref-Game_AI_173-0" Jump up^? HYPERLINK "" \o "Game AI" Game AI:CITATION IN PROGRESS.[ HYPERLINK "" \o "Wikipedia:Citation needed" citation needed] HYPERLINK "" \l "cite_ref-Mathematical_definitions_of_intelligence_174-0" Jump up^?Mathematical definitions of intelligence:Jose Hernandez-Orallo (2000). "Beyond the Turing Test".?Journal of Logic, Language and Information?9?(4): 447–466.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1023/A:1008367325700.? HYPERLINK "" \l "CiteSeerX" \o "CiteSeer" CiteSeerX: HYPERLINK "" 10.1.1.44.8943.D L Dowe and A R Hajek (1997).? HYPERLINK "" "A computational extension to the Turing Test".?Proceedings of the 4th Conference of the Australasian Cognitive Science jSociety. Retrieved 21 July 2009.J Hernandez-Orallo and D L Dowe (2010). "Measuring Universal Intelligence: Towards an Anytime Intelligence Test".?Artificial Intelligence Journal?174?(18): 1508–1539.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1016/j.artint.2010.09.006. HYPERLINK "" \l "cite_ref-175" Jump up^?O'Brien and Marakas, 2011, Management Information Systems 10th ed. HYPERLINK "" \l "cite_ref-176" Jump up^? HYPERLINK "" "AI set to exceed human brain power"?(web article). CNN. 26 July 2006.? HYPERLINK "" Archived?from the original on 19 February 2008. Retrieved 26 February 2008. HYPERLINK "" \l "cite_ref-Intrusion_detection_177-0" Jump up^ HYPERLINK "" \l "CITEREFKumar2012" Kumar 2012 HYPERLINK "" \l "cite_ref-178" Jump up^?Brooks, R.A., "How to build complete creatures rather than isolated cognitive simulators," in K. VanLehn (ed.), Architectures for Intelligence, pp. 225–239, Lawrence Erlbaum Associates, Hillsdale, NJ, 1991. HYPERLINK "" \l "cite_ref-179" Jump up^? HYPERLINK "" Hacking Roomba?? Search Results?? atmel HYPERLINK "" \l "cite_ref-Philosophy_of_AI_180-0" Jump up^? HYPERLINK "" \o "Philosophy of AI" Philosophy of AI. All of these positions in this section are mentioned in standard discussions of the subject, such as: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?947–960 HYPERLINK "" \l "CITEREFFearn2007" Fearn 2007, pp.?38–55 HYPERLINK "" \l "cite_ref-Dartmouth_proposal_181-0" Jump up^? HYPERLINK "" \o "Dartmouth Conferences" Dartmouth proposal: HYPERLINK "" \l "CITEREFMcCarthyMinskyRochesterShannon1955" McCarthy et al. 1955?(the original proposal) HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, p.?49 (historical significance) HYPERLINK "" \l "cite_ref-Physical_symbol_system_hypothesis_182-0" Jump up^?The? HYPERLINK "" \o "Physical symbol system" physical symbol systems?hypothesis: HYPERLINK "" \l "CITEREFNewellSimon1976" Newell & Simon 1976, p.?116 HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, p.?153 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?18 HYPERLINK "" \l "cite_ref-183" Jump up^?Dreyfus criticized the? HYPERLINK "" \o "Necessary and sufficient" necessary?condition of the? HYPERLINK "" \o "Physical symbol system" physical symbol system?hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules". ( HYPERLINK "" \l "CITEREFDreyfus1992" Dreyfus 1992, p.?156) HYPERLINK "" \l "cite_ref-Dreyfus.27_critique_184-0" Jump up^? HYPERLINK "" \o "Dreyfus' critique of artificial intelligence" Dreyfus' critique of artificial intelligence: HYPERLINK "" \l "CITEREFDreyfus1972" Dreyfus 1972,? HYPERLINK "" \l "CITEREFDreyfusDreyfus1986" Dreyfus & Dreyfus 1986 HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?120–132 HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?211–239 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?950–952, HYPERLINK "" \l "cite_ref-The_mathematical_objection_185-0" Jump up^?The Mathematical Objection: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?949 HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?448–449Making the Mathematical Objection: HYPERLINK "" \l "CITEREFLucas1961" Lucas 1961 HYPERLINK "" \l "CITEREFPenrose1989" Penrose 1989Refuting Mathematical Objection: HYPERLINK "" \l "CITEREFTuring1950" Turing 1950?under "(2) The Mathematical Objection" HYPERLINK "" \l "CITEREFHofstadter1979" Hofstadter 1979Background:G?del 1931, Church 1936, Kleene 1935, Turing 1937 HYPERLINK "" \l "cite_ref-Searle.27s_strong_AI_186-0" Jump up^?This version is from? HYPERLINK "" \l "CITEREFSearle1999" Searle (1999), and is also quoted in HYPERLINK "" \l "CITEREFDennett1991" Dennett 1991, p.?435. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." ( HYPERLINK "" \l "CITEREFSearle1980" Searle 1980, p.?1). Strong AI is defined similarly by HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig (2003, p.?947): "The assertion that machines could possibly act intelligently (or, perhaps better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that machines that do so are actually thinking (as opposed to simulating thinking) is called the 'strong AI' hypothesis." HYPERLINK "" \l "cite_ref-Chinese_room_187-0" Jump up^?Searle's? HYPERLINK "" \o "Chinese room" Chinese room?argument: HYPERLINK "" \l "CITEREFSearle1980" Searle 1980. Searle's original presentation of the thought experiment. HYPERLINK "" \l "CITEREFSearle1999" Searle 1999.Discussion: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?958–960 HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?443–445 HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?269–271 HYPERLINK "" \l "cite_ref-recurse_188-0" Jump up^? HYPERLINK "" \o "Steve Omohundro" Omohundro, Steve?(2008). "The Nature of Self-Improving Arti?cial Intelligence". presented and distributed at the 2007 Singularity Summit, San Francisco, CA. HYPERLINK "" \l "cite_ref-Ford2009Lights_189-0" Jump up^?Ford, Martin R. (2009),? HYPERLINK "" The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future, Acculant Publishing,? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/978-1448659814" 978-1448659814.( HYPERLINK "" e-book available free online.) HYPERLINK "" \l "cite_ref-190" Jump up^? HYPERLINK "" "Machine Learning: A Job Killer?" HYPERLINK "" \l "cite_ref-Replaced_by_machines_191-0" Jump up^?AI could decrease the demand for human labor: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, pp.?960–961Ford, Martin (2009).? HYPERLINK "" The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future. Acculant Publishing.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/978-1-4486-5981-4" 978-1-4486-5981-4. HYPERLINK "" \l "cite_ref-192" Jump up^?In the early 1970s,? HYPERLINK "" \o "Kenneth Colby" Kenneth Colby?presented a version of Weizenbaum's? HYPERLINK "" \o "ELIZA" ELIZA?known as DOCTOR which he promoted as a serious therapeutic tool. ( HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?132–144) HYPERLINK "" \l "cite_ref-Weizenbaum.27s_critique_193-0" Jump up^? HYPERLINK "" \o "Joseph Weizenbaum" Joseph Weizenbaum's critique of AI: HYPERLINK "" \l "CITEREFWeizenbaum1976" Weizenbaum 1976 HYPERLINK "" \l "CITEREFCrevier1993" Crevier 1993, pp.?132–144 HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck 2004, pp.?356–373 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?961Weizenbaum (the AI researcher who developed the first HYPERLINK "" \o "Chatterbot" chatterbot?program,? HYPERLINK "" \o "ELIZA" ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. HYPERLINK "" \l "cite_ref-Singularity_194-0" Jump up^? HYPERLINK "" \o "Technological singularity" Technological singularity: HYPERLINK "" \l "CITEREFVinge1993" Vinge 1993 HYPERLINK "" \l "CITEREFKurzweil2005" Kurzweil 2005 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?963 HYPERLINK "" \l "cite_ref-Transhumanism_195-0" Jump up^? HYPERLINK "" \o "Transhumanism" Transhumanism: HYPERLINK "" \l "CITEREFMoravec1988" Moravec 1988 HYPERLINK "" \l "CITEREFKurzweil2005" Kurzweil 2005 HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?963 HYPERLINK "" \l "cite_ref-196" Jump up^? HYPERLINK "" \o "Charles T. Rubin" Rubin, Charles?(Spring 2003).? HYPERLINK "" "Artificial Intelligence and Human Nature".?The New Atlantis?1: 88–100. HYPERLINK "" \l "cite_ref-AI_as_evolution_197-0" Jump up^?AI as evolution: HYPERLINK "" \o "Edward Fredkin" Edward Fredkin?is quoted in? HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck (2004, p.?401). HYPERLINK "(novelist)" \o "Samuel Butler (novelist)" Butler, Samuel.? HYPERLINK "" "Darwin among the Macdate=13 June 1863".?the Press?(Christchurch, New Zealand), Letter to the Editor. HYPERLINK "(science_historian)" \o "George Dyson (science historian)" Dyson, George?(1998).?Darwin among the Machines. Allan Lane Science.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-7382-0030-1" 0-7382-0030-1. HYPERLINK "" \l "cite_ref-Robot_rights_198-0" Jump up^? HYPERLINK "" \o "Robot rights" Robot rights: HYPERLINK "" \l "CITEREFRussellNorvig2003" Russell & Norvig 2003, p.?964 HYPERLINK "" "Robots could demand legal rights".?BBC News. 21 December 2006. Retrieved 3 February 2011.Prematurity of:Henderson, Mark (24 April 2007).? HYPERLINK "" "Human rights for robots? We're getting carried away".?The Times Online?(London).In fiction: HYPERLINK "" \l "CITEREFMcCorduck2004" McCorduck (2004, p.?190-25) discusses? HYPERLINK "" \o "Frankenstein" Frankensteinand identifies the key ethical issues as scientific hubris and the suffering of the monster, i.e.? HYPERLINK "" \o "Robot rights" robot rights. HYPERLINK "" \l "cite_ref-199" Jump up^? HYPERLINK "" Independent documentary Plug & Pray, featuring Joseph Weizenbaum and Raymond KurzweilCite error: A? HYPERLINK "" \l "WP:LDR" \o "Help:Footnotes" list-defined reference?named "Definition_of_AI" is not used in the content (see the HYPERLINK "" \o "Help:Cite errors/Cite error references missing key" help page).References[ HYPERLINK "" \o "Edit section: References" edit]AI textbooks[ HYPERLINK "" \o "Edit section: AI textbooks" edit] HYPERLINK "" \o "Marcus Hutter" Hutter, Marcus?(2005).? HYPERLINK "" \o "AIXI" Universal Artificial Intelligence. Berlin: Springer.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/978-3-540-22139-5" 978-3-540-22139-5. HYPERLINK "" \o "George Luger (page does not exist)" Luger, George;? HYPERLINK "" \o "William Stubblefield (page does not exist)" Stubblefield, William?(2004).? HYPERLINK "" Artificial Intelligence: Structures and Strategies for Complex Problem Solving?(5th ed.). The Benjamin/Cummings Publishing Company, Inc.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-8053-4780-1" 0-8053-4780-1.Neapolitan, Richard; Jiang, Xia (2012).? HYPERLINK "" Contemporary Artificial Intelligence. Chapman & Hall/CRC.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/978-1-4398-4469-4" 978-1-4398-4469-4. HYPERLINK "(researcher)" \o "Nils Nilsson (researcher)" Nilsson, Nils?(1998).?Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/978-1-55860-467-4" 978-1-55860-467-4. HYPERLINK "" \o "Stuart J. Russell" Russell, Stuart J.;? HYPERLINK "" \o "Peter Norvig" Norvig, Peter?(2003),? HYPERLINK "" Artificial Intelligence: A Modern Approach?(2nd ed.), Upper Saddle River, New Jersey: Prentice Hall,? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-13-790395-2" 0-13-790395-2 HYPERLINK "(researcher)&action=edit&redlink=1" \o "David Poole (researcher) (page does not exist)" Poole, David;? HYPERLINK "" \o "Alan Mackworth" Mackworth, Alan;? HYPERLINK "" \o "Randy Goebel (page does not exist)" Goebel, Randy?(1998).? HYPERLINK "" Computational Intelligence: A Logical Approach. New York: Oxford University Press.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-19-510270-3" 0-19-510270-3. HYPERLINK "" \o "Patrick Winston" Winston, Patrick Henry?(1984).?Artificial Intelligence. Reading, Massachusetts: Addison-Wesley.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-201-08259-4" 0-201-08259-4. HYPERLINK "" \o "Elaine Rich (page does not exist)" Rich, Elaine?(1983).?Artificial Intelligence. McGraw-Hill.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-07-052261-8" 0-07-052261-8.History of AI[ HYPERLINK "" \o "Edit section: History of AI" edit] HYPERLINK "" \o "Daniel Crevier" Crevier, Daniel?(1993),?AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks,? HYPERLINK "" ISBN 0-465-02997-3 HYPERLINK "" \o "Pamela McCorduck" McCorduck, Pamela?(2004),? HYPERLINK "" Machines Who Think?(2nd ed.), Natick, MA: A. K. Peters, Ltd.,? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/1-56881-205-1" 1-56881-205-1 HYPERLINK "(researcher)" \o "Nils Nilsson (researcher)" Nilsson, Nils?(2010),?The Quest for Artificial Intelligence: A History of Ideas and Achievements, New York: Cambridge University Press,? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/978-0-521-12293-1" 978-0-521-12293-1Other sources[ HYPERLINK "" \o "Edit section: Other sources" edit] HYPERLINK "" "ACM Computing Classification System: Artificial intelligence".? HYPERLINK "" \o "Association for Computing Machinery" ACM. 1998. Retrieved 30 August 2007. HYPERLINK "" \o "Igor Aleksander" Aleksander, Igor?(1995).? HYPERLINK "" Artificial Neuroconsciousness: An Update. IWANN. Archived from? HYPERLINK "" the original?on 2 March 1997. HYPERLINK "" BibTex? HYPERLINK "" Archived?March 2, 1997 at the? HYPERLINK "" \o "Wayback Machine" Wayback Machine HYPERLINK "" \o "Rodney Brooks" Brooks, Rodney?(1990).? HYPERLINK "" "Elephants Don't Play Chess"?(PDF).?Robotics and Autonomous Systems?6: 3–15.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1016/S0921-8890(05)80025-9.? HYPERLINK "" Archived?from the original on 9 August 2007. Retrieved 30 August 2007..Buchanan, Bruce G. (2005).? HYPERLINK "" "A (Very) Brief History of Artificial Intelligence"?(PDF).?AI Magazine: 53–60.? HYPERLINK "" Archived?from the original on 26 September 2007. Retrieved 30 August 2007. HYPERLINK "" \o "Daniel Dennett" Dennett, Daniel?(1991).? HYPERLINK "" \o "Consciousness Explained" Consciousness Explained. The Penguin Press.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-7139-9037-6" 0-7139-9037-6. HYPERLINK "" \o "Hubert Dreyfus" Dreyfus, Hubert?(1972).? HYPERLINK "" \o "What Computers Can't Do" What Computers Can't Do. New York: MIT Press.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-06-011082-1" 0-06-011082-1. HYPERLINK "" \o "Hubert Dreyfus" Dreyfus, Hubert?(1979).?What Computers?Still?Can't Do. New York: MIT Press.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-262-04134-0" 0-262-04134-0. HYPERLINK "" \o "Hubert Dreyfus" Dreyfus, Hubert; Dreyfus, Stuart (1986).?Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. Oxford, UK: Blackwell.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-02-908060-6" 0-02-908060-6. HYPERLINK "" \o "Hubert Dreyfus" Dreyfus, Hubert?(1992).?What Computers?Still?Can't Do. New York: MIT Press.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-262-54067-3" 0-262-54067-3. HYPERLINK "" \o "Gerald Edelman" Edelman, Gerald?(23 November 2007).? HYPERLINK "" "Gerald Edelman – Neural Darwinism and Brain-based Devices". Talking Robots.Fearn, Nicholas (2007).?The Latest Answers to the Oldest Questions: A Philosophical Adventure with the World's Greatest Thinkers. New York: Grove Press.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-8021-1839-9" 0-8021-1839-9. HYPERLINK "" \o "Dion Forster" Forster, Dion?(2006).? HYPERLINK "" "Self validating consciousness in strong artificial intelligence: An African theological contribution". Pretoria: University of South Africa. HYPERLINK "" \o "Malcolm Gladwell" Gladwell, Malcolm?(2005).? HYPERLINK "(book)" \o "Blink (book)" Blink. New York: Little, Brown and Co.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-316-17232-4" 0-316-17232-4. HYPERLINK "" \o "John Haugeland" Haugeland, John?(1985).?Artificial Intelligence: The Very Idea. Cambridge, Mass.: MIT Press.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-262-08153-9" 0-262-08153-9. HYPERLINK "" \o "Jeff Hawkins" Hawkins, Jeff; Blakeslee, Sandra (2005).? HYPERLINK "" \o "On Intelligence" On Intelligence. New York, NY: Owl Books.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-8050-7853-3" 0-8050-7853-3. HYPERLINK "" \o "Douglas Hofstadter" Hofstadter, Douglas?(1979).? HYPERLINK "" \o "G?del, Escher, Bach" G?del, Escher, Bach: an Eternal Golden Braid. New York, NY: Vintage Books.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-394-74502-7" 0-394-74502-7.Howe, J. (November 1994).? HYPERLINK "" "Artificial Intelligence at Edinburgh University: a Perspective". Retrieved 30 August 2007.. HYPERLINK "" \o "Daniel Kahneman" Kahneman, Daniel; Slovic, D.;? HYPERLINK "" \o "Amos Tversky" Tversky, Amos?(1982).?Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-521-28414-7" 0-521-28414-7.Kolata, G. (1982). "How can computers get common sense?".?Science?217?(4566): 1237–1238. HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1126/science.217.4566.1237.? HYPERLINK "" \o "PubMed Identifier" PMID? HYPERLINK "" 17837639. HYPERLINK "" \o "Ray Kurzweil" Kurzweil, Ray?(1999).? HYPERLINK "" \o "The Age of Spiritual Machines" The Age of Spiritual Machines. Penguin Books.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-670-88217-8" 0-670-88217-8. HYPERLINK "" \o "Ray Kurzweil" Kurzweil, Ray?(2005).? HYPERLINK "" \o "The Singularity is Near" The Singularity is Near. Penguin Books.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-670-03384-7" 0-670-03384-7. HYPERLINK "" \o "George Lakoff" Lakoff, George?(1987).?Women, Fire, and Dangerous Things: What Categories Reveal About the Mind. University of Chicago Press. HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-226-46804-6" 0-226-46804-6. HYPERLINK "" \o "George Lakoff" Lakoff, George;? HYPERLINK "" \o "Rafael E. Nú?ez" Nú?ez, Rafael E.?(2000).? HYPERLINK "" \o "Where Mathematics Comes From" Where Mathematics Comes From: How the Embodied Mind Brings Mathematics into Being. Basic Books.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-465-03771-2" 0-465-03771-2.. HYPERLINK "" \o "Douglas Lenat" Lenat, Douglas; Guha, R. V. (1989).?Building Large Knowledge-Based Systems. Addison-Wesley.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-201-51752-3" 0-201-51752-3. HYPERLINK "" \o "James Lighthill" Lighthill, Professor Sir James?(1973). "Artificial Intelligence: A General Survey". "Artificial Intelligence: a paper symposium". Science Research Council. HYPERLINK "(philosopher)" \o "John Lucas (philosopher)" Lucas, John?(1961).? HYPERLINK "" "Minds, Machines and G?del". In Anderson, A.R.?Minds and Machines.? HYPERLINK "" Archived?from the original on 19 August 2007. Retrieved 30 August 2007.Maker, Meg Houston (2006).? HYPERLINK "" "AI@50: AI Past, Present, Future". Dartmouth College.? HYPERLINK "" Archived?from the original on 8 October 2008. Retrieved 16 October 2008. HYPERLINK "(computer_scientist)" \o "John McCarthy (computer scientist)" McCarthy, John;? HYPERLINK "" \o "Marvin Minsky" Minsky, Marvin;? HYPERLINK "" \o "Nathan Rochester" Rochester, Nathan;? HYPERLINK "" \o "Claude Shannon" Shannon, Claude?(1955).? HYPERLINK "" "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence".? HYPERLINK "" Archived?from the original on 26 August 2007. Retrieved 30 August 2007.. HYPERLINK "(computer_scientist)" \o "John McCarthy (computer scientist)" McCarthy, John; Hayes, P. J. (1969).? HYPERLINK "" "Some philosophical problems from the standpoint of artificial intelligence".?Machine Intelligence?4: 463–502.? HYPERLINK "" Archived?from the original on 10 August 2007. Retrieved 30 August 2007. HYPERLINK "(computer_scientist)" \o "John McCarthy (computer scientist)" McCarthy, John?(12 November 2007).? HYPERLINK "" "What Is Artificial Intelligence?". HYPERLINK "" \o "Marvin Minsky" Minsky, Marvin?(1967).?Computation: Finite and Infinite Machines. Englewood Cliffs, N.J.: Prentice-Hall.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-13-165449-7" 0-13-165449-7. HYPERLINK "" \o "Marvin Minsky" Minsky, Marvin?(2006).? HYPERLINK "" \o "The Emotion Machine" The Emotion Machine. New York, NY: Simon & Schusterl.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-7432-7663-9" 0-7432-7663-9. HYPERLINK "" \o "Hans Moravec" Moravec, Hans?(1976).? HYPERLINK "" "The Role of Raw Power in Intelligence". Retrieved 30 August 2007. HYPERLINK "" \o "Hans Moravec" Moravec, Hans?(1988).?Mind Children. Harvard University Press.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-674-57616-0" 0-674-57616-0. HYPERLINK "" \o "United States National Research Council" NRC, (United States National Research Council)?(1999). "Developments in Artificial Intelligence".?Funding a Revolution: Government Support for Computing Research. National Academy Press. HYPERLINK "" \o "Joseph Needham" Needham, Joseph?(1986).? HYPERLINK "" \o "Science and Civilization in China" Science and Civilization in China: Volume 2. Caves Books Ltd. HYPERLINK "" \o "Allen Newell" Newell, Allen;? HYPERLINK "" \o "Herbert A. Simon" Simon, H. A.?(1963). "GPS: A Program that Simulates Human Thought". In Feigenbaum, E.A.; Feldman, puters and Thought. New York: McGraw-Hill. HYPERLINK "" \o "Allen Newell" Newell, Allen;? HYPERLINK "" \o "Herbert A. Simon" Simon, H. A.?(1976).? HYPERLINK "" "Computer Science as Empirical Inquiry: Symbols and Search". "Communications of the ACM"?19?(3).. HYPERLINK "(researcher)" \o "Nils Nilsson (researcher)" Nilsson, Nils?(1983),? HYPERLINK "" Artificial Intelligence Prepares for 2001,?AI Magazine?1?(1), Presidential Address to the? HYPERLINK "" \o "Association for the Advancement of Artificial Intelligence" Association for the Advancement of Artificial Intelligence. HYPERLINK "" \o "Roger Penrose" Penrose, Roger?(1989).?The Emperor's New Mind: Concerning Computer, Minds and The Laws of Physics.? HYPERLINK "" \o "Oxford University Press" Oxford University Press. HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-19-851973-7" 0-19-851973-7. HYPERLINK "" \o "John Searle" Searle, John?(1980).? HYPERLINK "" "Minds, Brains and Programs".?Behavioral and Brain Sciences?3?(3): 417–457. HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1017/S0140525X00005756. HYPERLINK "" \o "John Searle" Searle, John?(1999).?Mind, language and society. New York, NY: Basic Books.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-465-04521-9" 0-465-04521-9.? HYPERLINK "" \o "OCLC" OCLC? HYPERLINK "" 231867665 43689264.Serenko, Alexander; Detlor, Brian (2004).? HYPERLINK "" "Intelligent agents as innovations".?AI and Society?18?(4): 364–381. HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1007/s00146-004-0310-5.Serenko, Alexander; Ruhi, Umar; Cocosila, Mihail (2007).? HYPERLINK "" "Unplanned effects of intelligent agents on Internet use: Social Informatics approach".?AI and Society?21?(1–2): 141–166.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1007/s00146-006-0051-8.Shapiro, Stuart C. (1992).? HYPERLINK "" "Artificial Intelligence". In Shapiro, Stuart C.?Encyclopedia of Artificial Intelligence?(2nd ed.). New York: John Wiley. pp.?54–57.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-471-50306-1" 0-471-50306-1. HYPERLINK "" \o "Herbert A. Simon" Simon, H. A.?(1965).?The Shape of Automation for Men and Management. New York: Harper & Row.Skillings, Jonathan (3 July 2006).? HYPERLINK "" "Getting Machines to Think Like Us".?cnet. Retrieved 3 February 2011.Tecuci, Gheorghe (March–April 2012). "Artificial Intelligence".?Wiley Interdisciplinary Reviews: Computational Statistics?(Wiley)?4(2): 168–180.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1002/wics.200. HYPERLINK "" \o "Alan Turing" Turing, Alan?(October 1950),? HYPERLINK "" Computing Machinery and Intelligence,? HYPERLINK "(journal)" \o "Mind (journal)" Mind?LIX?(236): 433–460,? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1093/mind/LIX.236.433, HYPERLINK "" \o "International Standard Serial Number" ISSN? HYPERLINK "" 0026-4423, retrieved 2008-08-18.van der Walt, Christiaan; Bernard, Etienne (2006<!––year is presumed based on acknowledgements at the end of the article––>). HYPERLINK "" "Data characteristics that determine classifier performance"?(PDF). Retrieved 5 August 2009.?Check date values in:?|date=( HYPERLINK "" \l "bad_date" \o "Help:CS1 errors" help) HYPERLINK "" \o "Vernor Vinge" Vinge, Vernor?(1993).? HYPERLINK "" "The Coming Technological Singularity: How to Survive in the Post-Human Era". HYPERLINK "" \o "Peter Cathcart Wason" Wason, P. C.; Shapiro, D. (1966). "Reasoning". In Foss, B. M.?New horizons in psychology. Harmondsworth: Penguin. HYPERLINK "" \o "Joseph Weizenbaum" Weizenbaum, Joseph?(1976).? HYPERLINK "" \o "Computer Power and Human Reason" Computer Power and Human Reason. San Francisco: W.H. Freeman & Company.? HYPERLINK "" \o "International Standard Book Number" ISBN? HYPERLINK "" \o "Special:BookSources/0-7167-0464-1" 0-7167-0464-1.Kumar, Gulshan; Krishan Kumar (2012).? HYPERLINK "" "The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review".Applied Computational Intelligence and Soft Computing?2012: 1–20.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1155/2012/850160. Retrieved 11 February 2013.Further reading[ HYPERLINK "" \o "Edit section: Further reading" edit]TechCast Article Series, John Sagi,? HYPERLINK "" Framing Consciousness HYPERLINK "" \o "Boden, Margaret (page does not exist)" Boden, Margaret, Mind As Machine,? HYPERLINK "" \o "Oxford University Press" Oxford University Press, 2006Johnston, John (2008) "The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI", MIT PressMyers, Courtney Boyd ed. (2009).? HYPERLINK "" The AI Report. Forbes June 2009Serenko, Alexander (2010).? HYPERLINK "" "The development of an AI journal ranking based on the revealed preference approach"(PDF).?Journal of Informetrics?4?(4): 447–459.? HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1016/j.joi.2010.04.001.Serenko, Alexander; Michael Dohan (2011).? HYPERLINK "" "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence"?(PDF).?Journal of Informetrics?5?(4): 629–649. HYPERLINK "" \o "Digital object identifier" doi: HYPERLINK "" 10.1016/j.joi.2011.06.002.Sun, R. & Bookman, L. (eds.),?Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994.External links[ HYPERLINK "" \o "Edit section: External links" edit]Find more about?Artificial Intelligenceat Wikipedia's?sister projectsDefinitions?from WiktionaryMedia?from CommonsQuotations?from WikiquoteSource texts?from WikisourceTextbooks?from WikibooksLearning resources?from Wikiversity HYPERLINK "" What Is AI??— An introduction to artificial intelligence by AI founder? 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