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Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory , decision analysis , [] and information value theory. 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 [] is perhaps the most widely used machine learning algorithm. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data.

Otherwise, if no matching model is available, and if accuracy rather than speed or scalability is the sole concern, conventional wisdom is that discriminative classifiers especially SVM tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets. Neural networks 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 neural network forms "concepts" that are distributed among a subnetwork of shared [j] neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot".

Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks can learn both continuous functions and, surprisingly, digital logical operations. Neural networks' early successes included predicting the stock market and in a mostly self-driving car. The study of non-learning 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.

Caianiello , and others [ citation needed ]. The main categories of networks are acyclic or feedforward neural networks where the signal passes in only one direction and recurrent neural networks which allow feedback and short-term memories of previous input events. Among the most popular feedforward networks are perceptrons , multi-layer perceptrons and radial basis networks. Today, neural networks are often trained by the backpropagation algorithm, which had been around since as the reverse mode of automatic differentiation published by Seppo Linnainmaa , [] [] and was introduced to neural networks by Paul Werbos.

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex. To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber , argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches [ citation needed ]. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends".

Deep learning is any artificial neural network that can learn a long chain of causal links [ dubious — discuss ]. Many deep learning systems need to be able to learn chains ten or more causal links in length. According to one overview, [] the expression "Deep Learning" was introduced to the machine learning community by Rina Dechter in [] and gained traction after Igor Aizenberg and colleagues introduced it to artificial neural networks in Lapa in Ivakhnenko's paper [] describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks.

In , a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks FNNs one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine , then using supervised backpropagation for fine-tuning.

Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. Deep learning often uses convolutional neural networks CNNs , whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind's " AlphaGo Lee", the program that beat a top Go champion in Early on, deep learning was also applied to sequence learning with recurrent neural networks RNNs [] which are in theory Turing complete [] and can run arbitrary programs to process arbitrary sequences of inputs.

The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning. 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 well-publicized benchmark for assessing rates of progress. AlphaGo around brought the era of classical board-game benchmarks to a close.

Games of imperfect knowledge provide new challenges to AI in the area of game theory. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars , and robot soccer as well as conventional games. The "imitation game" an interpretation of the Turing test that assesses whether a computer can imitate a human is nowadays considered too exploitable to be a meaningful benchmark. As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human.

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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. Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity ; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.

AI is relevant to any intellectual task. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect. High-profile examples of AI include autonomous vehicles such as drones and self-driving cars , medical diagnosis, creating art such as poetry , proving mathematical theorems, playing games such as Chess or Go , search engines such as Google search , online assistants such as Siri , image recognition in photographs, spam filtering, predicting flight delays, [] prediction of judicial decisions [] and targeting online advertisements.

With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution, [] major publishers now use artificial intelligence AI technology to post stories more effectively and generate higher volumes of traffic. AI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high risk patients for population health.

The breadth of applications is rapidly increasing. In , 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. Artificial intelligence is assisting doctors.

According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.

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In detail, there are more than medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover" [ citation needed ].

Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia , a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers. According to CNN , a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot.

The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed. Watson has struggled to achieve success and adoption in healthcare. Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles.

As of [update] , there are over 30 companies utilizing AI into the creation of driverless cars. Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle. Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.

One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings.

However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings. Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car.

But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers. Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties.

AI can react to changes overnight or when business is not taking place. AI is also being used by corporations. RPA uses artificial intelligence to train and teach software robots to process transactions, monitor compliance and audit processes automatically. The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades [ citation needed ]. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient [ citation needed ].

Other theories where AI has had impact include in rational choice , rational expectations , game theory , Lewis turning point , portfolio optimization and counterfactual thinking [ citation needed ]. Artificial intelligence paired with facial recognition systems may be used for mass surveillance. This is already the case in some parts of China. In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters NPCs. In addition, well-understood AI techniques are routinely used for pathfinding.

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. It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically. Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.

Artificial Intelligence has inspired numerous creative applications including its usage to produce visual art. Recent exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the deepdream algorithm [] and the exhibition "Unhuman: Art in the Age of AI," which took place in Los Angeles and Frankfurt in the fall of Widespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable.

Scientists from the Future of Life Institute , among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies. The potential negative effects of AI and automation are a major issue for Andrew Yang 's presidential campaign.

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 ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded. In his book Superintelligence , Nick Bostrom provides an argument that artificial intelligence will pose a threat to humankind. 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 AI's goals do not reflect humanity's—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal. Concern over risk from artificial intelligence has led to some high-profile donations and investments. The goal of the institute is to "grow wisdom with which we manage" the growing power of technology.

Musk also funds companies developing artificial intelligence such as Google DeepMind and Vicarious to "just keep an eye on what's going on with artificial intelligence. For this danger to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching. 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 [] 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 is now known as computationalism. To Weizenbaum these points suggest that AI research devalues human life.

One concern is that AI programs may be programmed to be biased against certain groups, such as women and minorities, because most of the developers are wealthy Caucasian men. Algorithms have a host of applications in today's legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants. The relationship between automation and employment is complicated.

While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects. Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that "we're in uncharted territory" with AI. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones. 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. 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.

In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this. In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence.

Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics. Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be benevolent. Hyper-intelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth.

One proposal to deal with this is to ensure that the first generally intelligent AI is ' Friendly AI ' and will be able to control subsequently developed AIs. Some question whether this kind of check could actually 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. 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. David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness. 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 "it's 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. The hard problem is explaining how the brain creates it, why it exists, and how it is different from knowledge and other aspects of the brain.

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. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam.

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. 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, California's Institute for the Future , although many critics believe that the discussion is premature.

Are there limits to how intelligent machines—or human-machine 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. If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement.

Science fiction writer Vernor Vinge named this scenario " singularity ". Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable. Ray Kurzweil has used Moore's law which describes the relentless exponential improvement in digital technology to calculate that desktop computers will have the same processing power as human brains by the year , and predicts that the singularity will occur in 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.

Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler 's " Darwin among the Machines " as far back as , and expanded upon by George Dyson in his book of the same name in The long-term economic effects of AI are uncertain. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment , but they generally agree that it could be a net benefit, if productivity gains are redistributed. Thought-capable artificial beings appeared as storytelling devices since antiquity, [24] and have been a persistent theme in science fiction.

A common trope in these works began with Mary Shelley 's Frankenstein , where a human creation becomes a threat to its masters. This includes such works as Arthur C. In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still and Bishop from Aliens are less prominent in popular culture. Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name.

Asimov's laws are often brought up during lay discussions of machine ethics; [] while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.

Transhumanism the merging of humans and machines is explored in the manga Ghost in the Shell and the science-fiction series Dune. In the s, artist Hajime Sorayama 's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including George Lucas and other creatives.

Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form. 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. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.

See also: Logic machines in fiction and List of fictional computers. From Wikipedia, the free encyclopedia. This is the latest accepted revision , reviewed on 20 September For other uses, see AI disambiguation and Artificial intelligence disambiguation. Intelligence demonstrated by machines. Main articles: History of artificial intelligence and Timeline of artificial intelligence.

Main articles: Knowledge representation and Commonsense knowledge. Main article: Automated planning and scheduling. Main article: Machine learning. Main article: Natural language processing. Main articles: Machine perception , Computer vision , and Speech recognition. Main article: Robotics.

Privacy, identity, and autonomy in the age of big data and AI - Sandra Wachter, University of Oxford

Main article: Affective computing. Main articles: Artificial general intelligence and AI-complete. Main articles: Cybernetics and Computational neuroscience. Main article: Symbolic AI. Main articles: Search algorithm , Mathematical optimization , and Evolutionary computation. Main articles: Logic programming and Automated reasoning. Expectation-maximization 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.

Main articles: Classifier mathematics , Statistical classification , and Machine learning. Main articles: Artificial neural network and Connectionism. Main article: Deep learning. Main article: Recurrent neural networks. Further information: Progress in artificial intelligence and Competitions and prizes in artificial intelligence.

Main article: Applications of artificial intelligence.

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Main article: Artificial intelligence in healthcare. Main article: driverless cars. Main article: Artificial intelligence in government. See also: Mass surveillance in China. Main article: Artificial intelligence video games. Further information: Artificial intelligence arms race , Lethal autonomous weapon , and Unmanned combat aerial vehicle. Further information: Computer art. Main articles: Philosophy of artificial intelligence and Ethics of artificial intelligence. Main article: Existential risk from artificial general intelligence.

Main article: Computer Power and Human Reason. See also: Lethal autonomous weapon. Main article: Machine ethics. Main article: Friendly AI. Main article: Artificial consciousness. Main articles: Hard problem of consciousness and Theory of mind. Main articles: Computationalism and Functionalism philosophy of mind.

Main article: Chinese room. Main article: Robot rights. Main article: Superintelligence. Main articles: Technological singularity and Moore's law. Main article: Transhumanism. Main article: Artificial intelligence in fiction. Computer programming portal. Abductive reasoning A. Rising Artificial intelligence arms race Behavior selection algorithm Business process automation Case-based reasoning Commonsense reasoning Emergent algorithm Evolutionary computation Glossary of artificial intelligence Machine learning Mathematical optimization Multi-agent system Personality computing Robotic process automation Soft computing Universal basic income Weak AI.

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