artificial intelligence ai redirects here for other uses see ai disambiguation and artificial intelligence disambiguation intelligence demonstrated by machines artificial intelligence major goals knowledge reasoning planning machine learning natural language processing computer vision robotics artificial general intelligence approaches symbolic deep learning bayesian networks evolutionary algorithms philosophy ethics existential risk turing test chinese room friendly ai history timeline progress ai winter technology applications projects programming languages glossary glossary vte artificial intelligence ai sometimes called machine intelligence is intelligence demonstrated by machines in contrast to the natural intelligence displayed by humans and other animals in computer science ai research is defined as the study of intelligent agents any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals the scope of ai is disputed as machines become increasingly capable tasks considered as requiring intelligence are often removed from the definition a phenomenon known as the ai effect leading to the quip in teslers theorem ai is whatever hasnt been done yet autonomously operating cars and intelligent routing in content delivery networks and military simulations artificial intelligence was founded as an academic discipline in 1956 and in the years since has experienced several waves of optimism the traditional problems or goals of ai research include reasoning knowledge representation planning learning natural language processing perception and the ability to move and manipulate objects approaches include statistical methods computational intelligence and traditional symbolic ai many tools are used in ai including versions of search and mathematical optimization artificial neural networks and methods based on statistics probability and economics the ai field draws upon computer science information engineering mathematics psychology linguistics philosophy and many others the field was founded on the claim that human intelligence can be so precisely described that a machine can be made to simulate it in the twentyfirst century ai techniques have experienced a resurgence following concurrent advances in computer power large amounts of data and theoretical understanding and ai techniques have become an essential part of the technology industry helping to solve many challenging problems in computer science software engineering and operations research history history of artificial intelligence and timeline of artificial intelligence talos an ancient mythical automaton with artificial intelligence thoughtcapable artificial beings appeared as storytelling devices in antiquity the study of mechanical or formal reasoning began with philosophers and mathematicians in antiquity the study of mathematical logic led directly to alan turings theory of computation which suggested that a machine by shuffling symbols as simple as 0 and 1 could simulate any conceivable act of mathematical deduction this insight that digital computers can simulate any process of formal reasoning is known as the churchturing thesis the field of ai research was born at a workshop at dartmouth college in 1956 they failed to recognize the difficulty of some of the remaining tasks progress slowed and in 1974 in response to the criticism of sir james lighthill a period when obtaining funding for ai projects was difficult in the early 1980s ai research was revived by the commercial success of expert systems in the late 1990s and early 21st century ai began to be used for logistics data mining medical diagnosis and other areas in 2011 a jeopardy quiz show exhibition match ibms question answering system watson defeated the two greatest jeopardy champions brad rutter and ken jennings by a significant margin this marked the completion of a significant milestone in the development of artificial intelligence as go is an extremely complex game more so than chess according to bloombergs jack clark 2015 was a landmark year for artificial intelligence with the number of software projects that use ai within google increased from a sporadic usage in 2012 to more than 2700 projects clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011 basics a typical ai perceives its environment and takes actions that maximize its chance of successfully achieving its goals ai often revolves around the use of algorithms an algorithm is a set of unambiguous instructions that a mechanical computer can execute if someone has a threat that is two in a row take the remaining square otherwise if a move forks to create two threats at once play that move otherwise take the center square if it is free otherwise if your opponent has played in a corner take the opposite corner otherwise take an empty corner if one exists otherwise take any empty square many ai algorithms are capable of learning from data they can enhance themselves by learning new heuristics strategies or rules of thumb that have worked well in the past or can themselves write other algorithms some of the learners described below including bayesian networks decision trees and nearestneighbor could theoretically if given infinite data time and memory learn to approximate any function including whatever combination of mathematical functions would best describe the entire world these learners could therefore in theory derive all possible knowledge by considering every possible hypothesis and matching it against the data in practice it is almost never possible to consider every possibility because of the phenomenon of combinatorial explosion where the amount of time needed to solve a problem grows exponentially much of ai research involves figuring out how to identify and avoid considering broad swaths of possibilities that are unlikely to be fruitful the earliest and easiest to understand approach to ai was symbolism such as formal logic if an otherwise healthy adult has a fever then they may have influenza a second more general approach is bayesian inference if the current patient has a fever adjust the probability they have influenza in suchandsuch way the third major approach extremely popular in routine business ai applications are analogizers such as svm and nearestneighbor after examining the records of known past patients whose temperature symptoms age and other factors mostly match the current patient x of those patients turned out to have influenza a fourth approach is harder to intuitively understand but is inspired by how the brains machinery works the artificial neural network approach uses artificial neurons that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to reinforce connections that seemed to be useful these four main approaches can overlap with each other and with evolutionary systems for example neural nets can learn to make inferences to generalize and to make analogies some systems implicitly or explicitly use multiple of these approaches alongside many other ai and nonai algorithms the blue line could be an example of overfitting a linear function due to random noise learning algorithms work on the basis that strategies algorithms and inferences that worked well in the past are likely to continue working well in the future these inferences can be obvious such as since the sun rose every morning for the last 10000 days it will probably rise tomorrow morning as well they can be nuanced such as x of families have geographically separate species with color variants so there is an y chance that undiscovered black swans exist learners also work on the basis of occams razor the simplest theory that explains the data is the likeliest therefore to be successful a learner must be designed such that it prefers simpler theories to complex theories except in cases where the complex theory is proven substantially better settling on a bad overly complex theory gerrymandered to fit all the past training data is known as overfitting many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data but penalizing the theory in accordance with how complex the theory is a selfdriving car system may use a neural network to determine which parts of the picture seem to match previous training images of pedestrians and then model those areas as slowmoving but somewhat unpredictable rectangular prisms that must be avoided compared with humans existing ai lacks several features of human commonsense reasoning most notably humans have powerful mechanisms for reasoning about nave physics such as space time and physical interactions this enables even young children to easily make inferences like if i roll this pen off a table it will fall on the floor humans also have a powerful mechanism of folk psychology that helps them to interpret naturallanguage sentences such as the city councilmen refused the demonstrators a permit because they advocated violence a generic ai has difficulty inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence problems the overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner the general problem of simulating or creating intelligence has been broken down into subproblems these consist of particular traits or capabilities that researchers expect an intelligent system to display the traits described below have received the most attention reasoning problem solving early researchers developed algorithms that imitated stepbystep reasoning that humans use when they solve puzzles or make logical deductions these algorithms proved to be insufficient for solving large reasoning problems because they experienced a combinatorial explosion they became exponentially slower as the problems grew larger knowledge representation an ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts knowledge representation and commonsense knowledge knowledge representation among the most difficult problems in knowledge representation are default reasoning and the qualification problem many of the things people know take the form of working assumptions for example if a bird comes up in conversation people typically picture an animal that is fist sized sings and flies none of these things are true about all birds john mccarthy identified this problem in 1969 the breadth of commonsense knowledge the number of atomic facts that the average person knows is very large research projects that attempt to build a complete knowledge base of commonsense knowledge eg cyc require enormous amounts of laborious ontological engineeringthey must be built by hand one complicated concept at a time the subsymbolic form of some commonsense knowledge much of what people know is not represented as facts or statements that they could express verbally for example a chess master will avoid a particular chess position because it feels too exposed planning a hierarchical control system is a form of control system in which a set of devices and governing software is arranged in a hierarchy automated planning and scheduling intelligent agents must be able to set goals and achieve them in classical planning problems the agent can assume that it is the only system acting in the world allowing the agent to be certain of the consequences of its actions multiagent 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 learning machine learning machine learning a fundamental concept of ai research since the fields inception unsupervised learning is the ability to find patterns in a stream of input without requiring a human to label the inputs first the agent is rewarded for good responses and punished for bad ones the agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space natural language processing a parse tree represents the syntactic structure of a sentence according to some formal grammar natural language processing natural language processing perception machine perception computer vision and speech recognition feature detection pictured edge detection helps ai compose informative abstract structures out of raw data machine perception motion and manipulation robotics ai is heavily used in robotics social intelligence affective computing kismet a robot with rudimentary social skills moravecs paradox can be extended to many forms of social intelligence in the long run social skills and an understanding of human emotion and game theory would be valuable to a social agent being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction or to otherwise facilitate humancomputer interaction general intelligence artificial general intelligence and aicomplete historically projects such as the cyc knowledge base 1984 and the massive japanese fifth generation computer systems initiative 19821992 attempted to cover the breadth of human cognition these early projects failed to escape the limitations of nonquantitative symbolic logic models and in retrospect greatly underestimated the difficulty of crossdomain ai nowadays the vast majority of current ai researchers work instead on tractable narrow ai applications such as medical diagnosis or automobile navigation many of the problems in this article may also require general intelligence if machines are to solve the problems as well as people do for example even specific straightforward tasks like machine translation require that a machine read and write in both languages nlp follow the authors argument reason know what is being talked about knowledge and faithfully reproduce the authors original intent social intelligence a problem like machine translation is considered aicomplete because all of these problems need to be solved simultaneously in order to reach humanlevel machine performance approaches there is no established unifying theory or paradigm that guides ai research researchers disagree about many issues can intelligent behavior be described using simple elegant principles such as logic or optimization or does it necessarily require solving a large number of completely unrelated problems cybernetics and brain simulation cybernetics and computational neuroscience in the 1940s and 1950s a number of researchers explored the connection between neurobiology information theory and cybernetics some of them built machines that used electronic networks to exhibit rudimentary intelligence such as w grey walters 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 by 1960 this approach was largely abandoned although elements of it would be revived in the 1980s symbolic symbolic ai when access to digital computers became possible in the middle 1950s ai research began to explore the possibility that human intelligence could be reduced to symbol manipulation the research was centered in three institutions carnegie mellon university stanford and mit and as described below each one developed its own style of research john haugeland named these symbolic approaches to ai good old fashioned ai or gofai 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 simulation economist herbert simon and allen newell studied human problemsolving skills and attempted to formalize them and their work laid the foundations of the field of artificial intelligence as well as cognitive science operations research and management science their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems this tradition centered at carnegie mellon university would eventually culminate in the development of the soar architecture in the middle 1980s logicbased unlike simon and newell john mccarthy felt that machines did not need to simulate human thought but should instead try to find the essence of abstract reasoning and problemsolving regardless of whether people used the same algorithms antilogic or scruffy researchers at mit such as marvin minsky and seymour papert knowledgebased when computers with large memories became available around 1970 researchers from all three traditions began to build knowledge into ai applications the knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple ai applications subsymbolic 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 subsymbolic approaches to specific ai problems subsymbolic methods manage to approach intelligence without specific representations of knowledge embodied intelligence this includes embodied situated behaviorbased and nouvelle ai researchers from the related field of robotics such as rodney brooks rejected symbolic ai and focused on the basic engineering problems that would allow robots to move and survive their work revived the nonsymbolic viewpoint of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in ai this coincided with the development of the embodied mind thesis in the related field of cognitive science the idea that aspects of the body such as movement perception and visualization are required for higher intelligence within developmental robotics developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous selfexploration social interaction with human teachers and the use of guidance mechanisms active learning maturation motor synergies etc computational intelligence and soft computing interest in neural networks and connectionism was revived by david rumelhart and others in the middle of the 1980s statistical learning much of traditional gofai got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to realworld results however around the 1990s ai researchers adopted sophisticated mathematical tools such as hidden markov models hmm information theory and normative bayesian decision theory to compare or to unify competing architectures the shared mathematical language permitted a high level of collaboration with more established fields like mathematics economics or operations research integrating the approaches intelligent agent paradigm an 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 allows researchers to directly compare or even combine different approaches to isolated problems by asking which agent is best at maximizing a given goal function an agent that solves a specific problem can use any approach that works some agents are symbolic and logical some are subsymbolic artificial neural networks and others may use new approaches the paradigm also gives researchers a common language to communicate with other fieldssuch as decision theory and economicsthat also use concepts of abstract agents building a complete agent requires researchers to address realistic problems of integration for example because sensory systems give uncertain information about the environment planning systems must be able to function in the presence of uncertainty the intelligent agent paradigm became widely accepted during the 1990s agent architectures and cognitive architectures researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multiagent system tools ai has developed a large number of tools to solve the most difficult problems in computer science a few of the most general of these methods are discussed below search and optimization search algorithm mathematical optimization and evolutionary computation many problems in ai can be solved in theory by intelligently searching through many possible solutions many learning algorithms use search algorithms based on optimization simple exhaustive searches 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 a particle swarm seeking the global minimum evolutionary computation uses a form of optimization search for example they may begin with a population of organisms the guesses and then allow them to mutate and recombine selecting only the fittest to survive each generation refining the guesses classic evolutionary algorithms include genetic algorithms gene expression programming and genetic programming logic logic programming and automated reasoning logic several different forms of logic are used in ai research propositional logic default logics nonmonotonic logics and circumscription overall qualitiative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty exceptions to rules are numerous and it is difficult for logical systems to function in the presence of contradictory rules probabilistic methods for uncertain reasoning bayesian network hidden markov model kalman filter particle filter decision theory and utility theory expectationmaximization clustering of old faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption many problems in ai in reasoning planning learning perception and robotics require the agent to operate with incomplete or uncertain information ai researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics bayesian networks 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 classifiers and statistical learning methods classifier mathematics statistical classification and machine learning the simplest ai applications can be divided into two types classifiers if shiny then diamond and controllers if shiny then pick up controllers do however also classify conditions before inferring actions and therefore classification forms a central part of many ai systems classifiers are functions that use pattern matching to determine a closest match they can be tuned according to examples making them very attractive for use in ai these examples are known as observations or patterns in supervised learning each pattern belongs to a certain predefined class a class can be seen as a decision that has to be made all the observations combined with their class labels are known as a data set when a new observation is received that observation is classified based on previous experience a classifier can be trained in various ways there are many statistical and machine learning approaches the decision tree knearest neighbor algorithm kernel methods such as the support vector machine svm gaussian mixture model artificial neural networks artificial neural network and connectionism a neural network is an interconnected group of nodes akin to the vast network of neurons in the human brain neural networks or neural nets were inspired by the architecture of neurons in the human brain a simple neuron n accepts input from multiple other neurons each of which when activated or fired cast a weighted vote for or against whether neuron n should itself activate learning requires an algorithm to adjust these weights based on the training data one simple algorithm dubbed fire together wire together is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another the net forms concepts that are distributed among a subnetwork of shared the study of nonlearning artificial neural networks began in the decade before the field of ai research was founded in the work of walter pitts and warren mccullouch frank rosenblatt invented the perceptron a learning network with a single layer similar to the old concept of linear regression early pioneers also include alexey grigorevich ivakhnenko teuvo kohonen stephen grossberg kunihiko fukushima christoph von der malsburg david willshaw shunichi amari bernard widrow john hopfield eduardo r caianiello and others the main categories of networks are acyclic or feedforward neural networks where the signal passes in only one direction and recurrent neural networks which allow feedback and shortterm memories of previous input events among the most popular feedforward networks are perceptrons multilayer perceptrons and radial basis networks today neural networks are often trained by the backpropagation algorithm which had been around since 1970 as the reverse mode of automatic differentiation published by seppo linnainmaa hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex in short most neural networks use some form of gradient descent on a handcreated neural topology however some research groups such as uber argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches one advantage of neuroevolution is that it may be less prone to get caught in dead ends deep feedforward neural networks deep learning deep learning is any artificial neural network that can learn a long chain of causal links for example a feedforward network with six hidden layers can learn a sevenlink causal chain six hidden layers output layer and has a credit assignment path cap depth of seven many deep learning systems need to be able to learn chains ten or more causal links in length according to one overview and gained traction after igor aizenberg and colleagues introduced it to artificial neural networks in 2000 deep learning often uses convolutional neural networks cnns whose origins can be traced back to the neocognitron introduced by kunihiko fukushima in 1980 since 2011 fast implementations of cnns on gpus have won many visual pattern recognition competitions cnns with 12 convolutional layers were used in conjunction with reinforcement learning by deepminds alphago lee the program that beat a top go champion in 2016 deep recurrent neural networks recurrent neural networks early on deep learning was also applied to sequence learning with recurrent neural networks rnns numerous researchers now use variants of a deep learning recurrent nn called the long shortterm memory lstm network published by hochreiter schmidhuber in 1997 and a plethora of other applications evaluating progress further information progress in artificial intelligence and competitions and prizes in artificial intelligence ai like electricity or the steam engine is a general purpose technology there is no consensus on how to characterize which tasks ai tends to excel at games provide a wellpublicized benchmark for assessing rates of progress alphago around 2016 brought the era of classical boardgame benchmarks to a close games of imperfect knowledge provide new challenges to ai in the area of game theory the imitation game an interpretation of the 1950 turing test that assesses whether a computer can imitate a human is nowadays considered too exploitable to be a meaningful benchmark proposed universal intelligence tests aim to compare how well machines humans and even nonhuman animals perform on problem sets that are generic as possible at an extreme the test suite can contain every possible problem weighted by kolmogorov complexity unfortunately these problem sets tend to be dominated by impoverished patternmatching exercises where a tuned ai can easily exceed human performance levels applications an automated online assistant providing customer service on a web page one of many very primitive applications of artificial intelligence applications of artificial intelligence ai is relevant to any intellectual task highprofile examples of ai include autonomous vehicles such as drones and selfdriving cars medical diagnosis creating art such as poetry proving mathematical theorems playing games such as chess or go search engines such as google search online assistants such as siri image recognition in photographs spam filtering prediction of judicial decisions with social media sites overtaking tv as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution healthcare artificial intelligence in healthcare a patientside surgical arm of da vinci surgical systemai is being applied to the high cost problem of dosage issueswhere findings suggested that ai could save 16 billion in 2016 a ground breaking study in california found that a mathematical formula developed with the help of ai correctly determined the accurate dose of immunosuppressant drugs to give to organ patients xray of a hand with automatic calculation of bone age by computer software artificial intelligence is breaking into the healthcare industry by assisting doctors according to bloomberg technology microsoft has developed ai to help doctors find the right treatments for cancer according to cnn a recent study by surgeons at the childrens national medical center in washington successfully demonstrated surgery with an autonomous robot the team supervised the robot while it performed softtissue surgery stitching together a pigs bowel during open surgery and doing so better than a human surgeon the team claimed automotive driverless cars advancements in ai have contributed to the growth of the automotive industry through the creation and evolution of selfdriving vehicles as of 2016 many components contribute to the functioning of selfdriving cars these vehicles incorporate systems such as braking lane changing collision prevention navigation and mapping together these systems as well as high performance computers are integrated into one complex vehicle recent developments in autonomous automobiles have made the innovation of selfdriving trucks possible though they are still in the testing phase the uk government has passed legislation to begin testing of selfdriving truck platoons in 2018 one main factor that influences the ability for a driverless automobile to function is mapping in general the vehicle would be preprogrammed with a map of the area being driven this map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings however google has been working on an algorithm with the purpose of eliminating the need for preprogrammed maps and instead creating a device that would be able to adjust to a variety of new surroundings another factor that is influencing the ability for a driverless automobile is the safety of the passenger to make a driverless automobile engineers must program it to handle highrisk situations these situations could include a headon collision with pedestrians the cars main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car but there is a possibility the car would need to make a decision that would put someone in danger in other words the car would need to decide to save the pedestrians or the passengers the programming of the car in these situations is crucial to a successful driverless automobile finance and economics financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm flagging these for human investigation the use of ai in banking can be traced back to 1987 when security pacific national bank in us setup a fraud prevention task force to counter the unauthorised use of debit cards programs like kasisto and moneystream are using ai in financial services banks use artificial intelligence systems today to organize operations maintain bookkeeping invest in stocks and manage properties ai can react to changes overnight or when business is not taking place the use of ai machines in the market in applications such as online trading and decision making has changed major economic theories for example ai based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing furthermore ai machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades furthermore ai in the markets limits the consequences of behavior in the markets again making markets more efficient other theories where ai has had impact include in rational choice rational expectations game theory lewis turning point portfolio optimization and counterfactual thinking video games artificial intelligence video games in video games artificial intelligence is routinely used to generate dynamic purposeful behavior in nonplayer characters npcs in addition wellunderstood ai techniques are routinely used for pathfinding some researchers consider npc ai in games to be a solved problem for most production tasks games with more atypical ai include the ai director of left 4 dead 2008 and the neuroevolutionary training of platoons in supreme commander 2 2010 military further information artificial intelligence arms race lethal autonomous weapon and unmanned combat aerial vehicle worldwide annual military spending on robotics rose from 51 billion usd in 2010 to 75 billion usd in 2015 audit for financial statements audit ai makes continuous audit possible ai tools could analyze many sets of different information immediately the potential benefit would be the overall audit risk will be reduced the level of assurance will be increased and the time duration of audit will be reduced advertising a report by the guardian newspaper in the uk in 2018 found that online gambling companies were using ai to predict the behavior of customers in order to target them with personalized promotions art artificial intelligence has inspired numerous creative applications including its usage to produce visual art the exhibition thinking machines art and design in the computer age 19591989 at moma philosophy and ethics philosophy of artificial intelligence and ethics of artificial intelligence there are three philosophical questions related to ai is artificial general intelligence possible can a machine solve any problem that a human being can solve using intelligence or are there hard limits to what a machine can accomplish are intelligent machines dangerous how can we ensure that machines behave ethically and that they are used ethically can a machine have a mind consciousness and mental states in exactly the same sense that human beings do can a machine be sentient and thus deserve certain rights can a machine intentionally cause harm the limits of artificial general intelligence philosophy of ai turing test physical symbol systems hypothesis dreyfus critique of ai the emperors new mind and ai effect can a machine be intelligent can it think alan turings 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 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 newell and simons physical symbol system hypothesis a physical symbol system has the necessary and sufficient means of general intelligent action newell and simon argue that intelligence consists of formal operations on symbols gdelian arguments gdel himself the artificial brain argument the brain can be simulated by machines and because brains are intelligent simulated brains must also be intelligent thus machines can be intelligent 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 the ai effect machines are already intelligent but observers have failed to recognize it when deep blue beat garry kasparov in chess the machine was acting intelligently however onlookers commonly discount the behavior of an artificial intelligence program by arguing that it is not real intelligence after all thus real intelligence is whatever intelligent behavior people can do that machines still cannot this is known as the ai effect ai is whatever hasnt been done yet potential harm widespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable scientists from the future of life institute among others described some shortterm research goals to see how ai influences the economy the laws and ethics that are involved with ai and how to minimize ai security risks in the longterm the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies existential risk existential risk from artificial general intelligence physicist stephen hawking microsoft founder bill gates and spacex founder elon musk have expressed concerns about the possibility that ai could evolve to the point that humans could not control it with hawking theorizing that this could spell the end of the human race the development of full artificial intelligence could spell the end of the human race once humans develop artificial intelligence it will take off on its own and redesign itself at an everincreasing rate humans who are limited by slow biological evolution couldnt compete and would be supersededstephen hawking in his book superintelligence nick bostrom provides an argument that artificial intelligence will pose a threat to mankind he argues that sufficiently intelligent ai if it chooses actions based on achieving some goal will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down if this ais goals do not reflect humanitys one example is an ai told to compute as many digits of pi as possible it might harm humanity in order to acquire more resources or prevent itself from being shut down ultimately to better achieve its goal concern over risk from artificial intelligence has led to some highprofile donations and investments a group of prominent tech titans including peter thiel amazon web services and musk have committed 1billion to openai a nonprofit company aimed at championing responsible ai development for this danger to be realized the hypothetical ai would have to overpower or outthink all of humanity which a minority of experts argue is a possibility far enough in the future to not be worth researching devaluation of humanity computer power and human reason joseph weizenbaum wrote that ai applications cannot by definition successfully simulate genuine human empathy and that the use of ai technology in fields such as customer service or psychotherapy decrease in demand for human labor further information technological unemployment 21st century the relationship between automation and employment is complicated while automation eliminates old jobs it also creates new jobs through microeconomic and macroeconomic effects autonomous weapons see also lethal autonomous weapon currently 50 countries are researching battlefield robots including the united states china russia and the united kingdom many people concerned about risk from superintelligent ai also want to limit the use of artificial soldiers and drones ethical machines machines with intelligence have the potential to use their intelligence to prevent harm and minimize the risks they may have the ability to use ethical reasoning to better choose their actions in the world research in this area includes machine ethics artificial moral agents and friendly ai artificial moral agents wendell wallach introduced the concept of artificial moral agents ama in his book moral machines machine ethics machine ethics the field of machine ethics is concerned with giving machines ethical principles or a procedure for discovering a way to resolve the ethical dilemmas they might encounter enabling them to function in an ethically responsible manner through their own ethical decision making malevolent and friendly ai friendly ai political scientist charles t rubin believes that ai can be neither designed nor guaranteed to be benevolent he argues that any sufficiently advanced benevolence may be indistinguishable from malevolence humans should not assume machines or robots would treat us favorably because there is no a priori reason to believe that they would be sympathetic to our system of morality which has evolved along with our particular biology which ais would not share hyperintelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop this topic has also recently begun to be discussed in academic publications as a real source of risks to civilization humans and planet earth 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 leading ai researcher rodney brooks writes i think it is a mistake to be worrying about us developing malevolent ai anytime in the next few hundred years i think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of ai and the enormity and complexity of building sentient volitional intelligence machine consciousness sentience and mind artificial consciousness if an ai system replicates all key aspects of human intelligence will that system also be sentient will it have a mind which has conscious experiences this question is closely related to the philosophical problem as to the nature of human consciousness generally referred to as the hard problem of consciousness consciousness hard problem of consciousness and theory of mind david chalmers identified two problems in understanding the mind which he named the hard and easy problems of consciousness the easy problem is understanding how the brain processes signals makes plans and controls behavior the hard problem is explaining how this feels or why it should feel like anything at all human information processing is easy to explain however human subjective experience is difficult to explain for example consider what happens when a person is shown a color swatch and identifies it saying its red the easy problem only requires understanding the machinery in the brain that makes it possible for a person to know that the color swatch is red the hard problem is that people also know something else they also know what red looks like consider that a person born blind can know that something is red without knowing what red looks like everyone knows subjective experience exists because they do it every day eg all sighted people know what red looks like the hard problem is explaining how the brain creates it why it exists and how it is different than knowledge and other aspects of the brain computationalism and functionalism computationalism and functionalism philosophy of mind computationalism is the position in the philosophy of mind that the human mind or the human brain or both is an information processing system and that thinking is a form of computing computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mindbody problem this philosophical position was inspired by the work of ai researchers and cognitive scientists in the 1960s and was originally proposed by philosophers jerry fodor and hilary putnam strong ai hypothesis chinese room the philosophical position that john searle has named strong ai states the appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds robot rights robot rights 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 this issue now known as robot rights is currently being considered by for example californias institute for the future although many critics believe that the discussion is premature superintelligence superintelligence are there limits to how intelligent machines or humanmachine hybrids can be a superintelligence hyperintelligence or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind superintelligence may also refer to the form or degree of intelligence possessed by such an agent technological singularity technological singularity and moores law 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 selfimprovement ray kurzweil has used moores 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 and predicts that the singularity will occur in 2045 transhumanism transhumanism you awake one morning to find your brain has another lobe functioning invisible this auxiliary lobe answers your questions with information beyond the realm of your own memory suggests plausible courses of action and asks questions that help bring out relevant facts you quickly come to rely on the new lobe so much that you stop wondering how it works you just use it this is the dream of artificial intelligencebyte april 1985 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 this idea called transhumanism which has roots in aldous huxley and robert ettinger edward fredkin argues that artificial intelligence is the next stage in evolution an idea first proposed by samuel butlers darwin among the machines 1863 and expanded upon by george dyson in his book of the same name in 1998 in fiction artificial intelligence in fiction the word robot itself was coined by karel apek in his 1921 play rur the title standing for rossums universal robots thoughtcapable artificial beings appeared as storytelling devices since antiquity and have been a persistent theme in science fiction a common trope in these works began with mary shelleys frankenstein where a human creation becomes a threat to its masters this includes such works as arthur c clarkes and stanley kubricks 2001 a space odyssey both 1968 with hal 9000 the murderous computer in charge of the discovery one spaceship as well as the terminator 1984 and the matrix 1999 in contrast the rare loyal robots such as gort from the day the earth stood still 1951 and bishop from aliens 1986 are less prominent in popular culture isaac asimov introduce the three laws of robotics in many books and stories most notably the multivac series about a superintelligent computer of the same name asimovs laws are often brought up during layman discussions of machine ethics transhumanism the merging of humans and machines is explored in the manga ghost in the shell and the sciencefiction series dune in the 1980s artist hajime sorayamas 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 several works use ai to force us to confront the fundamental of question of what makes us human showing us artificial beings that have the ability to feel and thus to suffer this appears in karel apeks rur the films ai artificial intelligence and ex machina as well as the novel do androids dream of electric sheep by philip k dick dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence artificial intelligence portal abductive reasoning behavior selection algorithm business process automation casebased reasoning commonsense reasoning emergent algorithm evolutionary computation glossary of artificial intelligence machine learning mathematical optimization multiagent system robotic process automation soft computing weak ai explanatory notes ai textbooks hutter marcus 2005 universal artificial intelligence berlin springer isbn9783540221395 jackson philip 1985 introduction to artificial intelligence 2nd ed dover isbn048624864x luger george stubblefield william 2004 artificial intelligence structures and 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from the natural kind scientific american vol 316 no 3 march 2017 pp5863 multiple tests of artificialintelligence efficacy are needed because just as there is no single test of athletic prowess there cannot be one ultimate test of intelligence one such test a construction challenge would test perception and physical actiontwo important elements of intelligent behavior that were entirely absent from the original turing test another proposal has been to give machines the same standardized tests of science and other disciplines that schoolchildren take a so far insuperable stumbling block to artificial intelligence is an incapacity for reliable disambiguation is ambiguous often in multiple ways a prominent example is known as the pronoun disambiguation problem a machine has no way of determining to whom or what a pronoun in a sentencesuch as he she or itrefers e mcgaughey will robots automate your job away full employment basic income and economic democracy 2018 ssrn part 23 myers courtney boyd ed 2009 the ai report forbes june 2009 raphael bertram 1976 the thinking computer whfreeman and company isbn0716707233 serenko alexander 2010 the development of an ai journal ranking based on the revealed preference approach pdf journal of informetrics 4 4 447459 doi101016jjoi201004001 serenko alexander michael dohan 2011 comparing the expert survey and citation impact journal ranking methods example from the field of artificial intelligence pdf journal of informetrics 5 4 629649 doi101016jjoi201106002 sun r bookman l eds computational architectures integrating neural and symbolic processes kluwer academic publishers needham ma 1994 tom simonite 29 december 2014 2014 in computing breakthroughs in artificial intelligence mit technology review artificial intelligenceat wikipedias sister projects definitions from wiktionary media from wikimedia commons news from wikinews quotations from wikiquote texts from wikisource textbooks from wikibooks resources from wikiversity what is ai an introduction to artificial intelligence by john mccarthya cofounder of the field and the person who coined the term the handbook of artificial intelligence volume by avron barr and edward a feigenbaum stanford university artificial intelligence internet encyclopedia of philosophy thomason richmond logic and artificial intelligence in zalta edward n stanford encyclopedia of philosophy ai at curlie aitopics a large directory of links and other resources maintained by the association for the advancement of artificial intelligence the leading organization of academic ai researchers list of ai conferences a list of 225 ai conferences taking place all over the world artificial intelligence bbc radio 4 discussion with john agar alison adam igor aleksander in our time dec 8 2005 links to related articles vtejohn mccarthy artificial intelligence circumscription dartmouth workshop frame problem garbage collection lisp mccarthy formalism mccarthy 91 function situation calculus space 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william of ockham hugh of saint victor dominicus gundissalinus robert kilwardby early modern francis bacon thomas hobbes ren descartes galileo galilei pierre gassendi isaac newton david hume late modern immanuel kant friedrich schelling william whewell auguste comte john stuart mill herbert spencer wilhelm wundt charles sanders peirce wilhelm windelband henri poincar pierre duhem rudolf steiner karl pearson contemporary alfred north whitehead bertrand russell albert einstein otto neurath c d broad michael polanyi hans reichenbach rudolf carnap karl popper carl gustav hempel w v o quine thomas kuhn imre lakatos paul feyerabend jrgen habermas ian hacking bas van fraassen larry laudan daniel dennett portal category vteevolutionary computationmain topics convergence evolutionary computing evolutionary algorithm evolutionary data mining evolutionary multimodal optimization humanbased evolutionary computation interactive evolutionary computation algorithms cellular evolutionary algorithm covariance matrix adaptation evolution strategy cmaes differential evolution evolutionary programming genetic algorithm genetic programming gene expression programming evolution strategy natural evolution strategy neuroevolution learning classifier system related techniques swarm intelligence ant colony optimization bees algorithm cuckoo search particle swarm optimization bacterial colony optimization metaheuristic methods grey wolf optimizer firefly algorithm harmony search gaussian adaptation memetic algorithm related topics artificial development artificial intelligence artificial life digital organism evolutionary robotics fitness function fitness landscape fitness approximation genetic operators interactive evolutionary computation no free lunch in search and optimization machine learning mating pool program synthesis journals evolutionary computation journal vtecomputable knowledgetopics andconcepts alphabet of human thought authority control automated reasoning commonsense knowledge commonsense reasoning computability formal system inference engine knowledge base knowledgebased systems knowledge engineering knowledge extraction knowledge representation knowledge retrieval library classification logic programming ontology personal knowledge base question answering semantic reasoner proposals andimplementations zairja ars magna1300 an essay towards a real character and a philosophical language1688 calculus ratiocinator and characteristica universalis1700 dewey decimal classification1876 begriffsschrift1879 mundaneum1910 logical atomism1918 tractatus logicophilosophicus1921 hilberts program1920s incompleteness theorem1931 world brain1938 memex1945 general problem solver1959 prolog1972 cyc1984 semantic web2001 evi2007 wolfram alpha2009 watson2011 siri2011 knowledge graph2012 wikidata2012 cortana2014 viv2016 in fiction the engine gullivers travels 1726 joe a logic named joe 1946 the librarian snow crash 1992 dr know ai artificial intelligence 2001 waterhouse the baroque cycle 2003 see also logic machines in fiction and list of fictional computers vtemajor fields of computer sciencenote this template roughly follows the 2012 acm computing classification systemhardware printed circuit board peripheral integrated circuit very large scale integration systems on chip socs energy consumption green computing electronic design automation hardware acceleration computer systemsorganization computer architecture embedded system realtime computing dependability networks network architecture network protocol network components network scheduler network performance evaluation network service software organization interpreter middleware virtual machine operating system software quality software notationsand tools programming paradigm programming language compiler domainspecific language modeling language software framework integrated development environment software configuration management software library software repository software development 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andcommunications ambient intelligence internet of things artificial intelligence applications of artificial intelligence progress in artificial intelligence machine translation mobile translation machine vision semantic web speech recognition atomtronics carbon nanotube fieldeffect transistor cybermethodology fourthgeneration optical discs 3d optical data storage holographic data storage gpgpu memory cbram fram millipede mram nram pram racetrack memory rram sonos optical computing rfid chipless rfid softwaredefined radio threedimensional integrated circuit manufacturing 3d printing claytronics molecular assembler utility fog materials science aerogel amorphous metal artificial muscle conductive polymer femtotechnology fullerene graphene hightemperature superconductivity hightemperature superfluidity linear acetylenic carbon metamaterials metamaterial cloaking metal foam multifunction structures nanotechnology carbon nanotubes molecular nanotechnology nanomaterials picotechnology 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nanorobotics powered exoskeleton selfreconfiguring modular robot swarm robotics uncrewed vehicle space sciencelaunch fusion rocket nonrocket spacelaunch mass driver orbital ring skyhook space elevator space fountain space tether reusable launch system propulsion beampowered propulsion ion thruster laser propulsion plasma propulsion engine helicon thruster vasimr nuclear pulse propulsion solar sail other interstellar travel propellant depot laser communication in space transportaerial adaptive compliant wing backpack helicopter delivery drone flying car highaltitude platform jet pack pulse detonation engine scramjet spaceplane supersonic transport land airless tire alternative fuel vehicle hydrogen vehicle driverless car ground effect train hyperloop maglev train personal rapid transit transit elevated bus vactrain vehicular communication systems pipeline pneumatic transport automated vacuum collection other antigravity cloak of invisibility digital scent technology force field plasma 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hibbard bill joy elon musk steve omohundro huw price martin rees stuart j russell jaan tallinn max tegmark frank wilczek roman yampolskiy eliezer yudkowsky sam harris other open letter on artificial intelligence ethics of artificial intelligence controversies and dangers of artificial general intelligence artificial intelligence as a global catastrophic risk superintelligence paths dangers strategies our final invention vtephilosophy of mindtheories behaviorism radical biological naturalism cognitive psychology mindbody dualism eliminative materialism emergent materialism emergentism epiphenomenalism functionalism idealism interactionism materialism monism nave realism neurophenomenology neutral monism occasionalism psychoanalysis parallelism phenomenalism phenomenology physicalism identity theory property dualism representational solipsism substance dualism concepts abstract object artificial intelligence chinese room cognition cognitive closure concept concept and object consciousness hard problem of consciousness hypostatic abstraction idea identity ingenuity intelligence intentionality introspection intuition language of thought materialism mental event mental image mental process mental property mental representation mind mindbody problem nonphysical entity new mysterianism pain privileged access problem of other minds propositional attitude qualia tabula rasa understanding zombie more related topics metaphysics philosophy of artificial intelligence information perception self portal category philosophers category project task force vtesubfields of and scientists involved in cyberneticssubfields polycontexturality secondorder cybernetics catastrophe theory connectionism control theory decision theory engineering cybernetics information theory semiotics synergetics biological cybernetics biosemiotics biomedical cybernetics biorobotics computational neuroscience homeostasis management cybernetics medical cybernetics neurocybernetics sociocybernetics emergence artificial intelligence cyberneticists maleyka abbaszadeh igor aleksander william ross ashby gregory bateson anthony stafford beer natalia bekhtereva claude bernard ludwig von bertalanffy valentin braitenberg gordon s brown walter bradford cannon manfred clynes heinz von foerster charles franois jay wright forrester jacque fresco buckminster fuller ranulph glanville ernst von glasersfeld pyotr grigorenko n katherine hayles francis heylighen erich von holst jason jixuan hu cliff joslyn stuart kauffman sergei p kurdyumov allenna leonard alexander lerner niklas luhmann warren mcculloch humberto maturana marian mazur margaret mead ulla mitzdorf talcott parsons gordon pask walter pitts qian xuesen alfred radcliffebrown robert trappl valentin turchin jakob von uexkll stuart umpleby francisco varela frederic vester charles geoffrey vickers john n warfield william grey walter kevin warwick norbert wiener jennifer wilby anthony wilden vtemajor fields of computer sciencenote this template roughly follows the 2012 acm computing classification systemhardware printed circuit board peripheral integrated circuit very large scale integration systems on chip socs energy consumption green computing electronic design automation hardware acceleration computer systemsorganization computer architecture embedded system realtime computing dependability networks network architecture network protocol network components network scheduler network performance evaluation network service software organization interpreter middleware virtual machine operating system software quality software notationsand tools programming paradigm programming language compiler domainspecific language modeling language software framework integrated development environment software configuration management software library software repository software development software development process requirements analysis software design software construction software deployment software maintenance programming team opensource model theory of computation model of computation formal language automata theory computational complexity theory logic semantics algorithms algorithm design analysis of algorithms algorithmic efficiency randomized algorithm computational geometry mathematicsof computing discrete mathematics probability statistics mathematical software information theory mathematical analysis numerical analysis informationsystems database management system information storage systems enterprise information system social information systems geographic information system decision support system process control system multimedia information system data mining digital library computing platform digital marketing world wide web information retrieval security cryptography formal methods security services intrusion detection system hardware security network security information security application security humancomputerinteraction interaction design social computing ubiquitous computing visualization accessibility concurrency concurrent computing parallel computing distributed computing multithreading multiprocessing artificialintelligence natural language processing knowledge representation and reasoning computer vision automated planning and scheduling search methodology control method philosophy of artificial intelligence distributed artificial intelligence machine learning supervised learning unsupervised learning reinforcement learning multitask learning crossvalidation graphics animation rendering image manipulation graphics processing unit mixed reality virtual reality image compression solid modeling appliedcomputing ecommerce enterprise software computational mathematics computational physics computational chemistry computational biology computational social science computational engineering computational healthcare digital art electronic publishing cyberwarfare electronic voting video games word processing operations research educational technology document management book category portal wikiproject commons vteevolutionary computationmain topics convergence evolutionary computing evolutionary algorithm evolutionary data mining evolutionary multimodal optimization humanbased evolutionary computation interactive evolutionary computation algorithms cellular evolutionary algorithm covariance matrix adaptation evolution strategy cmaes differential evolution evolutionary programming genetic algorithm genetic programming gene expression programming evolution strategy natural evolution strategy neuroevolution learning classifier system related techniques swarm intelligence ant colony optimization bees algorithm cuckoo search particle swarm optimization bacterial colony optimization metaheuristic methods grey wolf optimizer firefly algorithm harmony search gaussian adaptation memetic algorithm related topics artificial development artificial intelligence artificial life digital organism evolutionary robotics fitness function fitness landscape fitness approximation genetic operators interactive evolutionary computation no free lunch in search and optimization machine learning mating pool program synthesis journals evolutionary computation journal vteemerging technologiesfieldsagriculture agricultural robot closed ecological systems cultured meat genetically modified food precision agriculture vertical farming architecture arcology building printing contour crafting domed city biomedical artificial uterus ampakine brain transplant cryonics cryoprotectant cryopreservation vitrification suspended animation deextinction genetic engineering gene therapy head transplant isolated brain life extension strategies for engineered negligible senescence nanomedicine nanosensors organ printing personalized medicine regenerative medicine stemcell therapy tissue engineering robotassisted surgery synthetic biology synthetic genomics virotherapy oncolytic virus tricorder whole genome sequencing displaysnext generation fed flcd imod laser lpd oled olet qdled sed tpd tdel tmos screenless bionic contact lens headmounted display headup display optical headmounted display virtual retinal display other autostereoscopy flexible display holographic display computergenerated holography multiprimary color display ultra hd volumetric display electronics electronic nose etextiles flexible electronics molecular electronics nanoelectromechanical systems memristor spintronics thermal copper pillar bump energyproduction airborne wind turbine artificial photosynthesis biofuels carbonneutral fuel concentrated solar power fusion power home fuel cell hydrogen economy methanol economy molten salt reactor nantenna photovoltaic pavement spacebased solar power vortex engine storage beltway battery compressed air energy storage flywheel energy storage grid energy storage lithiumair battery moltensalt battery nanowire battery research in lithiumion batteries siliconair battery thermal energy storage ultracapacitor other smart grid wireless power information andcommunications ambient intelligence internet of things artificial intelligence applications of artificial intelligence progress in artificial intelligence machine translation mobile translation machine vision semantic web speech recognition atomtronics carbon nanotube fieldeffect transistor cybermethodology fourthgeneration optical discs 3d optical data storage holographic data storage gpgpu memory cbram fram millipede mram nram pram racetrack memory rram sonos optical computing rfid chipless rfid softwaredefined radio threedimensional integrated circuit manufacturing 3d printing claytronics molecular assembler utility fog materials science aerogel amorphous metal artificial muscle conductive polymer femtotechnology fullerene graphene hightemperature superconductivity hightemperature superfluidity linear acetylenic carbon metamaterials metamaterial cloaking metal foam multifunction structures nanotechnology carbon nanotubes molecular nanotechnology nanomaterials picotechnology programmable matter quantum dots silicene superalloy synthetic diamond military antimatter weapon caseless ammunition directedenergy weapon laser maser particlebeam weapon sonic weapon coilgun railgun plasma weapon pure fusion weapon stealth technology vortex ring gun neuroscience artificial brain braincomputer interface electroencephalography mind uploading brainreading neuroinformatics neuroprosthetics bionic eye brain implant exocortex retinal implant quantum quantum algorithms quantum amplifier quantum bus quantum channel quantum circuit quantum complexity theory quantum computing quantum cryptography quantum dynamics quantum electronics quantum error correction quantum imaging quantum information quantum key distribution quantum logic quantum logic gates quantum machine quantum machine learning quantum metamaterial quantum metrology quantum network quantum neural network quantum optics quantum programming quantum sensing quantum simulator quantum teleportation robotics domotics nanorobotics powered exoskeleton selfreconfiguring modular robot swarm robotics uncrewed vehicle space sciencelaunch fusion rocket nonrocket spacelaunch mass driver orbital ring skyhook space elevator space fountain space tether reusable launch system propulsion beampowered propulsion ion thruster laser propulsion plasma propulsion engine helicon thruster vasimr nuclear pulse propulsion solar sail other interstellar travel propellant depot laser communication in space transportaerial adaptive compliant wing backpack helicopter delivery drone flying car highaltitude platform jet pack pulse detonation engine scramjet spaceplane supersonic transport land airless tire alternative fuel vehicle hydrogen vehicle driverless car ground effect train hyperloop maglev train personal rapid transit transit elevated bus vactrain vehicular communication systems pipeline pneumatic transport automated vacuum collection other antigravity cloak of invisibility digital scent technology force field plasma window immersive virtual reality magnetic refrigeration phasedarray optics topics collingridge dilemma differential technological development disruptive innovation ephemeralization exploratory engineering fictional technology proactionary principle technological change technological unemployment technological convergence technological evolution technological paradigm technology forecasting accelerating change moores law technological singularity technology scouting technology readiness level technology roadmap transhumanism category list vteglossaries of science and engineering aerospace engineering archaeology architecture artificial intelligence astronomy biology botany calculus chemistry civil engineering clinical research computer science ecology economics electrical and electronics engineering engineering entomology environmental science genetics geography geology machine vision mathematics mechanical engineering medicine meteorology physics probability and statistics psychiatry robotics speciation structural engineering authority control bnf cb11932084t data gnd 40334478 lccn sh85008180 ndl 00574798 