Mis New Project.

IntroductionArtificial Intelligence can be defined as an area having over half a century of the history. First of all in the late 1940s, the emergence of the computers took place and it was during this phase only that the Artificial Intelligence began in the earnest.These machines have the ability to store huge amount of the data and after this step these machines process it into the information at a very high speed. Although the Artificial Intelligence was born in the 1940s but it did not receive a great response from the various users at that particular time. It was only in the 1980s that the Artificial Intelligence received the popular economic and the managerial acclaim. All along during this period, a large amount of the transition took place in the concept of the Artificial Intelligence and one of the main transitions included the transition from a primary research area to the potential commercial applications.After this period of the major transitions only, the Artificial Intelligence was accepted as an emerging technology and got a very hot response from the different types of the users using it. The major reason of its acceptance was the fact that the Artificial Intelligence does not replace people but in fact the Artificial Intelligence liberate the experts from solving the common and the simple types of the problems, hence in turn leaving the experts for solving the various complex problems.One of the major advantages of the Artificial Intelligence is that it helps to avoid making the mistakes and also helps in responding very quickly to any type of the problem that may arise.Meaning and the DefinitionGeorge Luger and William Stabblefied defined Artificial Intelligence as a branch of the computer science that is mainly concerned with the automation of the intelligent behavior.Dan Patterson defined Artificial Intelligence as a branch of the computer science concerned with the study and the creation of the computer systems that exhibit some form of the intelligence: systems that learn the new concepts and the tasks, systems that can reason and also draw the useful conclusions about the world around us, systems that can under stand the various natural languages and perceive and comprehend a visual scene and the systems that perform the other types of the feats that essentially require the human types of the intelligence.Artificial Intelligence can be under stood as the technology playing a very major part in the application of the computers to the areas or the fields, which requires the basic knowledge, the perception, the reasoning, the understanding and the cognitive abilities. By having all this, it really becomes possible to distinguish the human behavior from the machines like the computers etc. Artificial Intelligence actually is the science and the engineering involving the making of the intelligent machines and one major point to be remembered here is that the Artificial Intelligence is related a great deal to the similar task of making use of the computers in order to under stand the human intelligence. Human intelligence is also referred to as the natural intelligence and the below explained comparison between the Natural Intelligence and the Artificial Intelligence helps a great deal in under standing the concept of both the Artificial Intelligence and the Natural Intelligence and the basic differences that occur between them.www.mbaofficial.com/mba-courses/.

HistoryMain articles:History of artificial intelligenceandTimeline of artificial intelligenceThinking machines and artificial beings appear inGreek myths, such asTalosofCrete, the bronze robot ofHephaestus, andPygmalion'sGalatea.[13]Human likenesses believed to have intelligence were built in every major civilization: animatedcult imageswere worshiped inEgyptandGreece[14]and humanoidautomatonswere built byYan Shi,Hero of AlexandriaandAl-Jazari.[15]It was also widely believed that artificial beings had been created byJbir ibn Hayyn,Judah LoewandParacelsus.[16]By the 19th and 20th centuries, artificial beings had become a common feature in fiction, as inMary Shelley'sFrankensteinorKarel apek'sR.U.R. (Rossum's Universal Robots).[17]Pamela McCorduckargues that all of these are examples of an ancient urge, as she describes it, "to forge the gods".[9]Stories of these creatures and their fates discuss many of the same hopes, fears andethical concernsthat are presented by artificial intelligence.Mechanical or"formal" reasoninghas been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of theprogrammable digital electronic computer, based on the work of mathematicianAlan Turingand others. Turing'stheory of computationsuggested 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 inneurology,information theoryandcybernetics, 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 ata conferenceon the campus ofDartmouth Collegein the summer of 1956.[21]The attendees, includingJohn McCarthy,Marvin Minsky,Allen NewellandHerbert Simon, became the leaders of AI research for many decades.[22]They and their students wrote programs that were, to most people, simply astonishing:[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 theDepartment 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 Simonpredicted that "machines will be capable, within twenty years, of doing any work a man can do" andMarvin Minskyagreed, 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 ofSir James Lighthilland 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",[29]a period when funding for AI projects was hard to find.In the early 1980s, AI research was revived by the commercial success ofexpert systems,[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'sfifth generation computerproject inspired the U.S and British governments to restore funding for academic research in the field.[31]However, beginning with the collapse of theLisp Machinemarket in 1987, AI once again fell into disrepute, and a second, longer lastingAI winterbegan.[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 diagnosisand many other areas throughout the technology industry.[12]The success was due to several factors: the increasing computational power of computers (seeMoore'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 Bluebecame the first computer chess-playing system to beat a reigning world chess champion,Garry Kasparov.[34]In 2005, a Stanford robot won theDARPA Grand Challengeby driving autonomously for 131 miles along an unrehearsed desert trail.[35]Two years later, a team fromCMUwon theDARPA Urban Challengewhen their vehicle autonomously navigated 55 miles in an urban environment while adhering to traffic hazards and all traffic laws.[36]In 2010,Definition of intelligence based on primitive semantics reveals that the intelligence which is usually said is a human level one.[37]In February 2011, in aJeopardy!quiz showexhibition match,IBM'squestion answering system,Watson, defeated the two greatest Jeopardy champions,Brad RutterandKen Jennings, by a significant margin.[38]TheKinect, which provides a 3D bodymotion interface for theXbox 360and the Xbox One, uses algorithms that emerged from lengthy AI research[39]as does the iPhone'sSiri.

The Advantages for Artificial Intelligence (AI) Jobs- depending on the level and type of intelligence these machines receive in the future, it will obviously have an effect on the type of work they can do, and how well they can do it (they can become more efficient). As the level of AI increases so will their competency to deal with difficult, complex even dangerous tasks that are currently done by humans, a form of applied artificial intelligence. They don't stop- as they are machines there is no need for sleep, they don't get ill , there is no need for breaks or Facebook, they are able to go, go, go! There obviously may be the need for them to be charged or refueled, however the point is, they are definitely going to get a lot more work done than we can. Take the Finance industry for example, there are constant stories arising of artificial intelligence in finance and that stock traders are soon to be a thing of the past. No risk of harm- when we are exploring new undiscovered land or even planets, when a machine gets broken or dies, there is no harm done as they don't feel, they don't have emotions. Where as going on the same type of expeditions a machine does, may simply not be possible or they are exposing themselves to high risk situations. Act as aids-they can act as 24/7 aids to children with disabilities or the elderly, they could even act as a source for learning and teaching. They could even be part of security alerting you to possible fires that you are in threat of, or fending off crime. Their function is almost limitless- as the machines will be able to do everything (but just better) essentially their use, pretty much doesn't have any boundaries. They will make fewer mistakes, they are emotionless, they are more efficient, they arebasically giving us more free time to do as we please.The Disadvantages for Artificial Intelligence (AI) Over reliance on AI-as you may have seen in many films such asThe Matrix, iRobot or even kids films such as WALL.E, if we rely on machines to do almost everything for us we become very dependent, so much so they have the potential to ruin our lives if something were to go wrong. Although the films are essentially just fiction, it wouldn't be too smart not to have some sort of back up plan to potential issues on our part. Human Feel-as they are are machines they obviously can't provide you with that 'human touch and quality', the feeling of a togetherness and emotional understanding, that machines will lack the ability to sympathise and empathise with your situations, and may act irrationally as a consequence. Inferior-as machines will be able to perform almost every task better than us in practically all respects, they will take up many of our jobs, which will then result in masses of people who are then jobless and as a result feel essentially useless. This could then lead us to issues of mental illness and obesity problems etc. Misuse-there is no doubt that this level of technology in the wrong hands can cause mass destruction, where robot armies could be formed, or they could perhaps malfunction or be corrupted which then we could be facing a similar scene to that of terminator ( hey, you never know). Ethically Wrong?-People say that the gift of intuition and intelligence was God's gift to mankind, and so to replicate that would be then to kind of 'play God'. Therefore not right to even attempt to clone our intelligence.

Applications of artificial intelligenceArtificial intelligencehas been used in a wide range of fields includingmedical diagnosis,stock trading,robot control,law,remote sensing, scientific discovery and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore,"Nick Bostromreports.[1]"Many thousands of AI applications are deeply embedded in the infrastructure of every industry."[2]In the late 90s and early 21st century, AI technology became widely used as elements of larger systems,[2][3]but the field is rarely credited for these successes.FinanceBanks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulatedfinancial tradingcompetition.[4]Financial institutionshave long usedartificial neural networksystems to detect charges or claims outside of the norm, flagging these for human investigation. AI is more S/W related so the game can be easier or harder.Hospitals and medicineA medical clinic can use artificial intelligence systems to organize bed schedules, make a staff rotation, and provide medical information.Artificial neural networksare used asclinical decision support systemsformedical diagnosis, such as inConcept Processingtechnology inEMRsoftware.Other tasks in medicine that can potentially be performed by artificial intelligence include: Computer-aided interpretation of medical images. Such systems help scan digital images,e.g.fromcomputed tomography, for typical appearances and to highlight conspicuous sections, such as possible diseases. A typical application is the detection of a tumor. Heart soundanalysis[5]Heavy industryRobotshave become common in many industries. They are often given jobs that are considered dangerous to humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading.Japanis the leader in using and producing robots in the world. In 1999, 1,700,000 robots were in use worldwide. For more information, see survey[6]about artificial intelligence in business.Online and telephone customer service

Anautomated online assistantprovidingcustomer serviceon a web page.Artificial intelligence is implemented inautomated online assistantsthat can be seen asavatarson web pages.[7]It can avail for enterprises to reduce their operation and training cost.[7]A major underlying technology to such systems isnatural language processing.[7]Similar techniques may be used inanswering machinesofcall centres, such asspeech recognitionsoftware to allow computers to handle first level ofcustomer support,text miningandnatural language processingto allow better customer handling, agent training by automatic mining ofbest practicesfrom past interactions,support automationand many other technologies to improve agent productivity andcustomer satisfaction.[8]TransportationFuzzy logiccontrollers have been developed for automatic gearboxes in automobiles (the 2006 Audi TT, VW Toureg[citation needed]and VW Caravell feature the DSP transmission which utilizes Fuzzy Logic, a number of koda variants (koda Fabia) also currently include a Fuzzy Logic based controller).TelecommunicationsMany telecommunications companies make use ofheuristic searchin the management of their workforces, for exampleBT Grouphas deployed heuristic search[9]in a scheduling application that provides the work schedules of 20,000 engineers.Toys and gamesThe 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with theDigital Revolution, and helped introduce people, especially children, to a life of dealing with various types of Artificial Intelligence, specifically in the form ofTamagotchisandGiga Pets,iPod Touch, theInternet(example: basic search engine interfaces are one simple form), and the first widely released robot,Furby. A mere year later an improved type ofdomestic robotwas released in the form ofAibo, a robotic dog with intelligent features andautonomy. AI has also beenapplied to video games.MusicThe evolution of music has always been affected by technology. With AI, scientists are trying to make the computeremulatethe activities of the skillful musician. Composition, performance, music theory, sound processing are some of the major areas on which research inMusic and Artificial Intelligenceare focusing.AviationThe Air Operations Division (AOD) uses AI for the rule basedexpert systems. The AOD has use forartificial intelligencefor surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.The use of artificial intelligence in simulators is proving to be very useful for the AOD. Airplane simulators are using artificial intelligence in order to process the data taken from simulated flights. Other than simulated flying, there is also simulated aircraft warfare. The computers are able to come up with the best success scenarios in these situations. The computers can also create strategies based on the placement, size, speed and strength of the forces and counter forces. Pilots may be given assistance in the air during combat by computers. The artificial intelligent programs can sort the information and provide the pilot with the best possible maneuvers, not to mention getting rid of certain maneuvers that would be impossible for a human being to perform. Multiple aircraft are needed to get good approximations for some calculations so computer simulated pilots are used to gather data. These computer simulated pilots are also used to train futureair traffic controllers.The system used by the AOD in order to measure performance was the Interactive Fault Diagnosis and Isolation System, or IFDIS. It is a rule based expert system put together by collecting information fromTF-30documents and the expert advice from mechanics that work on the TF-30. This system was designed to be used for the development of the TF-30 for the RAAF F-111C. The performance system was also used to replace specialized workers. The system allowed the regular workers to communicate with the system and avoid mistakes, miscalculations, or having to speak to one of the specialized workers.The AOD also uses artificial intelligence inspeech recognitionsoftware. The air traffic controllers are giving directions to the artificial pilots and the AOD wants to the pilots to respond to the ATC's with simple responses. The programs that incorporate the speech software must be trained, which means they useneural networks. The program used, the Verbex 7000, is still a very early program that has plenty of room for improvement. The improvements are imperative because ATCs use very specific dialog and the software needs to be able to communicate correctly and promptly every time.The Artificial Intelligence supported Design of Aircraft,[10]or AIDA, is used to help designers in the process of creating conceptual designs of aircraft. This program allows the designers to focus more on the design itself and less on the design process. The software also allows the user to focus less on the software tools. The AIDA uses rule based systems to compute its data. This is a diagram of the arrangement of the AIDA modules. Although simple, the program is proving effective.In 2003,NASA'sDryden Flight Research Center, and many other companies, created software that could enable a damaged aircraft to continue flight until a safe landing zone can be reached. The software compensates for all the damaged components by relying on the undamaged components. The neural network used in the software proved to be effective and marked a triumph for artificial intelligence.The Integrated Vehicle Health Management system, also used by NASA, on board an aircraft must process and interpret data taken from the various sensors on the aircraft. The system needs to be able to determine the structural integrity of the aircraft. The system also needs to implement protocols in case of any damage taken the vehicle.News, Publishing & WritingThe companyNarrative Sciencemakes computer generated news and reports commercially available, including summarizing team sporting events based on statistical data from the game in English. It also creates financial reports and real estate analyses.[11]Another company, calledYseop, uses artificial intelligence to turn structured data into intelligent comments and recommendations in natural language.Yseopis able to write financial reports, executive summaries, personalized sales or marketing documents and more at a speed of thousands of pages per second and in multiple languages including English, Spanish, French & German.[12]OtherVarious tools of artificial intelligence are also being widely deployed inhomeland security, speech and text recognition,data mining, ande-mail spamfiltering. Applications are also being developed forgesture recognition(understanding of sign language by machines), individualvoice recognition, global voice recognition (from a variety of people in a noisy room), facial expression recognition for interpretation of emotion and non verbal cues. Other applications arerobot navigation, obstacle avoidance, andobject recognition.[citation needed]

Artificial IntelligenceIs the MostImportantTechnology of the Future...

Artificial Intelligence is a set of tools that are driving forward key parts of the futurist agenda, sometimes at a rapid clip. The last few years have seen a slew of surprising advances: the IBM supercomputer Watson, which beat two champions ofJeopardy!; self-driving cars that have logged over 300,000 accident-free miles and are officially legal in three states; and statistical learning techniques are conducting pattern recognition on complex data sets from consumer interests to trillions of images. In this post, Ill bring you up to speed on what is happening in AI today, and talk about potential future applications. Any brief overview of AI will be necessarily incomplete, but Ill be describing a few of the most exciting items.The key applications of Artificial Intelligence are in any area that involves more data than humans can handle on our own, but which involves decisions simple enough that an AI can get somewhere with it. Big data, lots of little rote operations that add up to something useful. An example is image recognition; by doing rigorous, repetitive, low-level calculations on image features, we now have services likeGoogle Goggles, where you take an image of something, say a landmark, and Google tries to recognize what it is. Services like these are the first stirrings ofAugmented Reality(AR).Its easy to see how this kind of image recognition can be applied to repetitive tasks in biological research. One such difficult task is in brain mapping, an area that underlies dozens of transhumanist goals. The leader in this area is Sebastian Seung at MIT, who develops software to automatically determine the shape of neurons and locate synapses. Seung developed a fundamentally new kind of computer vision for automating work towards building connectomes, which detail the connections between all neurons. These are a key step to building computers that simulate the human brain.As an example of how difficult it is to build a connectome without AI, consider the case of the flatworm,C. elegans, the only completed connectome to date. Although electron microscopy was used to exhaustively map the brain of this flatworm in the 1970s and 80s, it took more than a decade of work to piece this data into a full map of the flatworms brain. This is despite that brain containing just 7000 connections between 300 neurons. By comparison, the human brain contains 100trillionconnections between 100 billion neurons. Without sophisticated AI, mapping it will be hopeless.Theres another closely related area that depends on AI to make progress; cognitive prostheses. These are brain implants that can perform the role of a part of the brain that has been damaged. Imagine a prosthesis that restores crucial memories to Alzheimers patients. The feasibility of a prosthesis of the hippocampus, part of the brain responsible for memory, was proven recently byTheodore Bergerat the University of Southern California. A rat with its hippocampus chemically disabled was able to form new memories with the aid of an implant.The way these implants are built is by carefully recording the neural signals of the brain and making a device that mimics the way they work. The device itself uses an artificial neural network, which Berger calls aHigh-density Hippocampal Neuron Network Processor. Painstaking observation of the brain region in question is needed to build amodel detailed enoughto stand in for the original. Without neural network techniques (a subcategory of AI) and abundant computing power, this approach would never work.Bringing the overview back to more everyday tech, consider all the AI that will be required to make thevisionof Augmented Reality mature. AR, as exemplified by Google Glass, uses computer glasses to overlay graphics on the real world. For the tech to work, it needs to quickly analyze what the viewer is seeing and generate graphics that provide useful information. To be useful, the glasses have to be able to identify complex objects from any direction, under any lighting conditions, no matter the weather. To be useful to a driver, for instance, the glasses would need to identify roads and landmarks faster and more effectively than is enabled by any current technology. AR is not there yet, but probably will be within the next ten years. All of this falls into the category of advances in computer vision, part of AI.Finally, lets consider some of the recent advances in building AI scientists. In 2009, Adam became the first robot todiscover new scientific knowledge, having to do with the genetics of yeast. The robot, which consists of a small room filled with experimental equipment connected to a computer, came up with its own hypothesis and tested it. Though the context and the experiment were simple, this milestone points to a new world of robotic possibilities. This is where the intersection between AI and other transhumanist areas, such as life extension research, could become profound.Many experiments in life science and biochemistry require a great deal of trial and error. Certain experiments are already automated with robotics, but what about computers that formulate and test their own hypotheses? Making this feasible would require the computer to understand a great deal of common sense knowledge, as well as specialized knowledge about the subject area. Consider a robot scientist like Adam with the object-level knowledge of theJeopardy!-winning Watson supercomputer. This could be built today in theory, but it will probably be a few years before anything like it is built in practice. Once it is, its difficult to say what the scientific returns could be, but they could be substantial. Well just have to build it and find out.That concludes this brief overview. There are many other interesting trends in AI, but machine vision, cognitive prostheses, and robotic scientists are among the most interesting, and relevant to futurist goals.I would like to thank Michael Anissimov, a fellow transhumanist and author of theAccelerating Future blog, for contributing this piece.

Branches of AI

logical AIWhat a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language. The program decides what to do by inferring that certain actions are appropriate for achieving its goals. The first article proposing this was [McC59]. [McC89] is a more recent summary. [McC96b] lists some of the concepts involved in logical aI. [Sha97] is an important text.searchAI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem proving program. Discoveries are continually made about how to do this more efficiently in various domains.pattern recognitionWhen a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied. These more complex patterns require quite different methods than do the simple patterns that have been studied the most.representationFacts about the world have to be represented in some way. Usually languages of mathematical logic are used.inferenceFrom some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods ofnon-monotonicinference have been added to logic since the 1970s. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary. For example, when we hear of a bird, we man infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin. It is the possibility that a conclusion may have to be withdrawn that constitutes the non-monotonic character of the reasoning. Ordinary logical reasoning is monotonic in that the set of conclusions that can the drawn from a set of premises is a monotonic increasing function of the premises. Circumscription is another form of non-monotonic reasoning.common sense knowledge and reasoningThis is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s. While there has been considerable progress, e.g. in developing systems ofnon-monotonic reasoningand theories of action, yet more new ideas are needed. The Cyc system contains a large but spotty collection of common sense facts.learning from experiencePrograms do that. The approaches to AI based onconnectionismandneural netsspecialize in that. There is also learning of laws expressed in logic. [Mit97] is a comprehensive undergraduate text on machine learning. Programs can only learn what facts or behaviors their formalisms can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information.planningPlanning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. From these, they generate a strategy for achieving the goal. In the most common cases, the strategy is just a sequence of actions.epistemologyThis is a study of the kinds of knowledge that are required for solving problems in the world.ontologyOntology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Emphasis on ontology begins in the 1990s.heuristicsA heuristic is a way of trying to discover something or an idea imbedded in a program. The term is used variously in AI.Heuristic functionsare used in some approaches to search to measure how far a node in a search tree seems to be from a goal.Heuristic predicatesthat compare two nodes in a search tree to see if one is better than the other, i.e. constitutes an advance toward the goal, may be more useful. [My opinion].genetic programmingGenetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations. It is being developed by John Koza's group and here's atutorial.

Features of AI Work: Use of symbolic reasoning . Focus on problems that do not respond to algorithmic solution (Heuristic) . Work on problems with inexact , missing , or poorly defined information . Provide answers that are sufficient but not exact . Deals with semantics as well as syntactic . Work with qualitative knowledge rather that quantitative knowledge . Use large amount of domain specific knowledge .

Comparison between intelligent computing and conventional computing:Intelligent ComputingConventional Computing

1Does not guarantee a solution to a given problem.1Guarantees a solution to a given problem.

2Results may not be reliable and consistent2Results are consistent and reliable.

3Programmer does not tell the system how to solve the given problem.3Programmer tells the system exactly how to solve the problem

4Can solve a range of problems in a given domain.4Can solve only one problem at a time in a given domain

GoalsThe 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.[6]Deduction, reasoning, problem solvingEarly AI researchers developed algorithms that imitated the step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[40]By the late 1980s and 1990s, AI research had also developed highly successful methods for dealing withuncertainor incomplete information, employing concepts fromprobabilityand economics.[41]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.[42]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.[43]AI has made some progress at imitating this kind of "sub-symbolic" problem solving:embodied agentapproaches emphasize the importance ofsensorimotorskills to higher reasoning;neural netresearch attempts to simulate the structures inside the brain that give rise to this skill;statistical approaches to AImimic the probabilistic nature of the human ability to guess.Knowledge representation

An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.Main articles:Knowledge representationandCommonsense knowledgeKnowledge representation[44]andknowledge engineering[45]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;[46]situations, events, states and time;[47]causes and effects;[48]knowledge about knowledge (what we know about what other people know);[49]and many other, less well researched domains. A representation of "what exists" is anontology: the set of objects, relations, concepts and so on that the machine knows about. The most general are calledupper ontologies, which attempt to provide a foundation for all other knowledge.[50]Among the most difficult problems in knowledge representation are:Default reasoningand thequalification 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 McCarthyidentified this problem in 1969[51]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.[52]The breadth ofcommonsense knowledgeThe number of atomic facts that the average person knows is astronomical. Research projects that attempt to build a complete knowledge base ofcommonsense knowledge(e.g.,Cyc) require enormous amounts of laboriousontological engineering they must be built, by hand, one complicated concept at a time.[53]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.[citation needed]The subsymbolic form of somecommonsense 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"[54]or an art critic can take one look at a statue and instantly realize that it is a fake.[55]These are intuitions or tendencies that are represented in the brain non-consciously and sub-symbolically.[56]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 thatsituated AI,computational intelligence, orstatistical AIwill provide ways to represent this kind of knowledge.[56]Planning

Ahierarchical control systemis a form ofcontrol systemin 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.[57]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 theutility(or "value") of the available choices.[58]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.[59]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.[60]Multi-agent planninguses thecooperationand competition of many agents to achieve a given goal.Emergent behaviorsuch as this is used byevolutionary algorithmsandswarm intelligence.[61]LearningMain article:Machine learningMachine learning is the study of computer algorithms that improve automatically through experience[62][63]and has been central to AI research since the field's inception.[64]Unsupervised learningis the ability to find patterns in a stream of input.Supervised learningincludes bothclassificationand numericalregression. 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. Inreinforcement learning[65]the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms ofdecision theory, using concepts likeutility. The mathematical analysis of machine learning algorithms and their performance is a branch oftheoretical computer scienceknown ascomputational learning theory.[66]Withindevelopmental 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.[67][68][69][70]Natural language processing

Aparse treerepresents thesyntacticstructure of a sentence according to someformal grammar.Main article:Natural language processingNatural language processing[71]gives machines the ability to read andunderstandthe languages that humans speak. A sufficiently powerful natural language processing system would enablenatural language user interfacesand the acquisition of knowledge directly from human-written sources, such as Internet texts. Some straightforward applications of natural language processing includeinformation retrieval(ortext mining) andmachine translation.[72]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 users input much more efficient.Motion and manipulationMain article:RoboticsThe field ofrobotics[73]is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation[74]andnavigation, with sub-problems oflocalization(knowing where you are, or finding out where other things are),mapping(learning what is around you, building a map of the environment), andmotion 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).[75][76]PerceptionMain articles:Machine perception,Computer vision, andSpeech recognitionMachine perception[77]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[78]is the ability to analyze visual input. A few selected subproblems arespeech recognition,[79]facial recognitionandobject recognition.[80]Social intelligenceMain article:Affective computing

Kismet, a robot with rudimentary social skills[81]Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate humanaffects.[82][83]It is an interdisciplinary field spanningcomputer sciences,psychology, andcognitive science.[84]While the origins of the field may be traced as far back as to early philosophical inquiries intoemotion,[85]the more modern branch of computer science originated withRosalind Picard's 1995 paper[86]on affective computing.[87][88]A motivation for the research is the ability to simulateempathy. 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[89]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 ofgame theory,decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions.) Also, in an effort to facilitatehuman-computer interaction, an intelligent machine might want to be able todisplayemotionseven if it does not actually experience them itselfin order to appear sensitive to the emotional dynamics of human interaction.CreativityMain article:Computational creativityA sub-field of AI addressescreativityboth 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 areArtificial intuitionandArtificial thinking.General intelligence[edit]Main articles:Artificial general intelligenceandAI-completeMany researchers think that their work will eventually be incorporated into a machine withgeneralintelligence (known asstrong AI), combining all the skills above and exceeding human abilities at most or all of them.[7]A few believe thatanthropomorphicfeatures likeartificial consciousnessor anartificial brainmay be required for such a project.[90][91]Many of the problems above may require general intelligence to be considered solved. For example, even a straightforward, specific task likemachine translationrequires 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 likemachine translationis considered "AI-complete". In order to solve this particular problem, you must solve all the problems.[92]Approaches[edit]There is no established unifying theory orparadigmthat guides AI research. Researchers disagree about many issues.[93]A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studyingpsychologyorneurology? Or is human biology as irrelevant to AI research as bird biology is toaeronautical engineering?[94]Can intelligent behavior be described using simple, elegant principles (such aslogicoroptimization)? Or does it necessarily require solving a large number of completely unrelated problems?[95]Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require "sub-symbolic" processing?[96]John Haugeland, who coined the term GOFAI (Good Old-Fashioned Artificial Intelligence), also proposed that AI should more properly be referred to assynthetic intelligence,[97]a term which has since been adopted by some non-GOFAI researchers.[98][99]Cybernetics and brain simulation[edit]Main articles:CyberneticsandComputational neuroscienceIn the 1940s and 1950s, a number of researchers explored the connection betweenneurology,information theory, andcybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such asW. Grey Walter'sturtlesand theJohns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society atPrinceton Universityand theRatio Clubin England.[20]By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.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,StanfordandMIT, and each one developed its own style of research.John Haugelandnamed these approaches to AI "good old fashioned AI" or "GOFAI".[100]During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based oncyberneticsorneural networkswere abandoned or pushed into the background.[101]Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine withartificial general intelligenceand considered this the goal of their field.Cognitive simulationEconomistHerbert SimonandAllen Newellstudied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well ascognitive science,operations researchandmanagement science. Their research team used the results ofpsychologicalexperiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered atCarnegie Mellon Universitywould eventually culminate in the development of theSoararchitecture in the middle 1980s.[102][103]Logic-basedUnlikeNewellandSimon,John McCarthyfelt 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.[94]His laboratory atStanford(SAIL) focused on using formallogicto solve a wide variety of problems, includingknowledge representation,planningandlearning.[104]Logic was also the focus of the work at theUniversity of Edinburghand elsewhere in Europe which led to the development of the programming languagePrologand the science oflogic programming.[105]"Anti-logic" or "scruffy"Researchers atMIT(such asMarvin MinskyandSeymour Papert)[106]found that solving difficult problems invisionandnatural language processingrequired ad-hoc solutions they argued that there was no simple and general principle (likelogic) that would capture all the aspects of intelligent behavior.Roger Schankdescribed their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms atCMUandStanford).[95]Commonsense knowledge bases(such asDoug Lenat'sCyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.[107]Knowledge-basedWhen computers with large memories became available around 1970, researchers from all three traditions began to buildknowledgeinto AI applications.[108]This "knowledge revolution" led to the development and deployment ofexpert systems(introduced byEdward 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 symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especiallyperception,robotics,learningandpattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.[96]Bottom-up,embodied,situated,behavior-basedornouvelle AIResearchers from the related field ofrobotics, such asRodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[109]Their work revived the non-symbolic viewpoint of the earlycyberneticsresearchers of the 1950s and reintroduced the use ofcontrol theoryin AI. This coincided with the development of theembodied mind thesisin the related field ofcognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.Computational intelligenceInterest inneural networksand "connectionism" was revived byDavid Rumelhartand others in the middle 1980s.[110]These and other sub-symbolic approaches, such asfuzzy systemsandevolutionary computation, are now studied collectively by the emerging discipline ofcomputational intelligence.[111]StatisticalIn the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are trulyscientific, 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 (likemathematics, economics oroperations research).Stuart RussellandPeter Norvigdescribe this movement as nothing less than a "revolution" and "the victory of theneats."[33]Critics argue that these techniques 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 betweenPeter NorvigandNoam Chomsky.[113][114]

Integrating the approaches

Intelligent agent paradigmAnintelligent agentis 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 asfirms). 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-symbolicneural networksand others may use new approaches. The paradigm also gives researchers a common language to communicate with other fieldssuch asdecision theoryand economicsthat also use concepts of abstract agents. The intelligent agent paradigm became widely accepted during the 1990s.[2]Agent architecturesandcognitive architecturesResearchers have designed systems to build intelligent systems out of interactingintelligent agentsin amulti-agent system.[115]A system with both symbolic and sub-symbolic components is ahybrid intelligent system, and the study of such systems isartificial intelligence systems integration. Ahierarchical control systemprovides 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.[116]Rodney Brooks'subsumption architecturewas an early proposal for such a hierarchical system.[117]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[edit]Main articles:Search algorithm,Mathematical optimization, andEvolutionary computationMany problems in AI can be solved in theory by intelligently searching through many possible solutions:[118]Reasoningcan be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads frompremisestoconclusions, where each step is the application of aninference rule.[119]Planningalgorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process calledmeans-ends analysis.[120]Roboticsalgorithms for moving limbs and grasping objects uselocal searchesinconfiguration space.[74]Manylearningalgorithms use search algorithms based onoptimization.Simple exhaustive searches[121]are rarely sufficient for most real world problems: thesearch space(the number of places to search) quickly grows toastronomicalnumbers. The result is a search that istoo slowor 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 "pruningthesearch tree").Heuristicssupply the program with a "best guess" for the path on which the solution lies.[122]Heuristics limit the search for solutions into a smaller sample size.[75]A very different kind of search came to prominence in the 1990s, based on the mathematical theory ofoptimization. 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 blindhill 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 aresimulated annealing,beam searchandrandom optimization.[123]Evolutionary computationuses 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,selectingonly the fittest to survive each generation (refining the guesses). Forms ofevolutionary computationincludeswarm intelligencealgorithms (such asant colonyorparticle swarm optimization)[124]andevolutionary algorithms(such asgenetic algorithms,gene expression programming, andgenetic programming).[125]Logic[edit]Main articles:Logic programmingandAutomated reasoningLogic[126]is used for knowledge representation and problemsolving, but it can be applied to other problems as well. For example, thesatplanalgorithm uses logic forplanning[127]andinductive logic programmingis a method forlearning.[128]Several different forms of logic are used in AI research.Propositionalorsentential logic[129]is the logic of statements which can be true or false.First-order logic[130]also allows the use ofquantifiersandpredicates, and can express facts about objects, their properties, and their relations with each other.Fuzzy logic,[131]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[132]models uncertainty in a different and more explicit manner than fuzzy-logic: a given binomial opinion satisfies belief + disbelief + uncertainty = 1 within aBeta distribution. By this method, ignorance can be distinguished from probabilistic statements that an agent makes with high confidence.Default logics,non-monotonic logicsandcircumscription[52]are forms of logic designed to help with default reasoning and thequalification problem. Several extensions of logic have been designed to handle specific domains ofknowledge, such as:description logics;[46]situation calculus,event calculusandfluent calculus(for representing events and time);[47]causal calculus;[48]belief calculus; andmodal logics.[49]Probabilistic methods for uncertain reasoning[edit]Main articles:Bayesian network,Hidden Markovmodel,Kalman filter,Decision theory, andUtility 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 fromprobabilitytheory and economics.[133]Bayesian networks[134]are a very general tool that can be used for a large number of problems: reasoning (using theBayesian inferencealgorithm),[135]learning(using theexpectation-maximization algorithm),[136]planning(usingdecision networks)[137]andperception(usingdynamic Bayesian networks).[138]Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helpingperceptionsystems to analyze processes that occur over time (e.g.,hidden Markov modelsorKalman filters).[138]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, usingdecision theory,decision analysis,[139]information value theory.[58]These tools include models such asMarkov decision processes,[140]dynamicdecision networks,[138]game theoryandmechanism design.[141]Classifiers and statistical learning methods[edit]Main articles:Classifier (mathematics),Statistical classification, andMachine 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.Classifiersare functions that usepattern matchingto 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.[142]A classifier can be trained in various ways; there are many statistical andmachine learningapproaches. The most widely used classifiers are theneural network,[143]kernel methodssuch as thesupport vector machine,[144]k-nearest neighbor algorithm,[145]Gaussian mixture model,[146]naive Bayes classifier,[147]anddecision tree.[148]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.[149]Neural networks[edit]Main articles:Neural networkandConnectionism

A neural network is an interconnected group of nodes, akin to the vast network ofneuronsin thehuman brain.The study ofartificial neural networks[143]began in the decade before the field AI research was founded, in the work ofWalter PittsandWarren McCullough. Other important early researchers wereFrank Rosenblatt, who invented theperceptronandPaul Werboswho developed thebackpropagationalgorithm.[150]The main categories of networks are acyclic orfeedforwardneural networks(where the signal passes in only one direction) andrecurrent neural networks(which allow feedback). Among the most popular feedforward networks areperceptrons,multi-layer perceptronsandradial basis networks.[151]Among recurrent networks, the most famous is theHopfield net, a form of attractor network, which was first described byJohn Hopfieldin 1982.[152]Neural networks can be applied to the problem ofintelligent control(for robotics) orlearning, using such techniques asHebbian learningandcompetitive learning.[153]Hierarchical temporal memoryis an approach that models some of the structural and algorithmic properties of theneocortex.[154]Control theory[edit]Main article:Intelligent controlControl theory, the grandchild ofcybernetics, has many important applications, especially inrobotics.[155]Languages[edit]Main article:List of programming languages for artificial intelligenceAI researchers have developed several specialized languages for AI research, includingLisp[156]andProlog.[157]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 theTuring 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.[158]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 termedsubject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.[159]One classification for outcomes of an AI test is:[160]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 atdraughts(i.e. checkers) is optimal,[161]performance at chess is super-human and nearing strong super-human (seecomputer 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 frommathematicaldefinitions of intelligence. Examples of these kinds of tests start in the late nineties devising intelligence tests using notions fromKolmogorov complexityanddata compression.[162]Two major advantages of mathematical definitions are their applicability to nonhuman intelligences and their absence of a requirement for human testers.An area that artificial intelligence had contributed greatly to is Intrusion detection.[163]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.[164]Applications[edit]

Anautomated online assistantproviding customer service on a web page one of many very primitive applications of artificial intelligence.

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 theAI effect.[165]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[edit]Aplatform(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[166]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 fromexpert systems, albeitPC-based but still an entire real-world system, to various robot platforms such as the widely availableRoombawith open interface.[167]Philosophy[edit]Main article:Philosophy of artificial intelligenceArtificial intelligence, by claiming to be able to recreate the capabilities of the humanmind, 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 amindandconsciousness? A few of the most influential answers to these questions are given below.[168]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 theTuring test.[158]TheDartmouth 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 theDartmouth Conferenceof 1956, and represents the position of most working AI researchers.[169]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.[170]Hubert Dreyfusargued 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. (SeeDreyfus' critique of AI.)[171][172]Gdel's incompleteness theoremAformal system(such as a computer program) cannot prove all true statements.[173]Roger Penroseis among those who claim that Gdel's theorem limits what machines can do. (SeeThe Emperor's New Mind.)[174]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."[175]John Searle counters this assertion with hisChinese roomargument, which asks us to lookinsidethe computer and try to find where the "mind" might be.[176]Theartificial brainargumentThe brain can be simulated.Hans Moravec,Ray Kurzweiland 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.[91]Predictions and ethics[edit]Main articles:Artificial intelligence in fiction,Ethics of artificial intelligence,Transhumanism, andTechnological singularityArtificial intelligence is a common topic in both science fiction and projections about the future of technology and society. The existence of an artificial intelligence that rivals human intelligence raises difficult ethical issues, and the potential power of the technology inspires both hopes and fears.In fiction, artificial intelligencehas appeared fulfilling many roles.These include:a real time battlefield analyst (CortanainHalo: Combat Evolved,Halo 2,Halo 3, andHalo 4)a servant (R2-D2andC-3POinStar Wars)a law enforcer (K.I.T.T."Knight Rider")a comrade (Lt. Commander DatainStar 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 QueeninResident Evil/ "Gilium" inOutlaw 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 OlivawinIsaac Asimov'sRobotseries)the human race critic and philosopher (Golem XIV)Mary Shelley'sFrankensteinconsiders a key issue in theethics of artificial intelligence: if a machine can be created that has intelligence, could it alsofeel? If it can feel, does it have the same rights as a human? The idea also appears in modern science fiction, including the filmsI Robot,Blade RunnerandA.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'sInstitute for the Future, although many critics believe that the discussion is premature.[177]The subject is profoundly discussed in the 2010 documentary filmPlug & Pray.[178]Martin Ford, author ofThe Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future,[179]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 occupationsand in particular entry level jobswill be increasingly susceptible to automation via expert systems, machine learning[180]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 tooutsourceknowledge work.[181]Joseph Weizenbaumwrote that AI applications can not, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such ascustomer serviceorpsychotherapy[182]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 ascomputationalism). To Weizenbaum these points suggest that AI research devalues human life.[183]Many futurists believe that artificial intelligence will ultimately transcend the limits of progress.Ray Kurzweilhas usedMoore's law(which describes the relentless exponential improvement in digital technology) to calculate thatdesktop computerswill have the same processing power as human brains by the year 2029. He also predictsthat by 2045 artificial intelligence will reach a point where it is able toimproveitselfat a rate that faexceeds anything conceivable in the past, a scenario that science fiction writerVernor Vingenamed the "singularity".[184]Robot designerHans Moravec, cyberneticistKevin Warwickand inventorRay Kurzweilhave predicted that humans and machines will merge in the future intocyborgsthat are more capable and powerful than either.[185]This idea, calledtranshumanism, which has roots inAldous HuxleyandRobert Ettinger, has been illustrated in fiction as well, for example in themangaGhost in the Shelland the science-fiction seriesDune. In the 1980s artistHajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with life-like muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers includingGeorge Lucasand 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 scientistCharles T. Rubinbelieves that AI can be neither designed nor guaranteed to befriendly.[186]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 noa priorireason 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 Fredkinargues that "artificial intelligence is the next stage in evolution", an idea first proposed bySamuel Butler's "Darwin among the Machines" (1863), and expanded upon byGeorge Dysonin his book of the same name in 1998.[187]