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A few studies were also centered on the economic aspects of the use of telerehabilitation to reduce the costs of hospitalization [ 15 ]. The main topics taken into account were 1 the status of telerehabilitation and analysis of the main medical specialties where it is being applied, 2 quality-of-life improvement due to telerehabilitation, and 3 the future of telerehabilitation. Parameters applied were English language, at least one keyword corresponding to the search terms in the title or abstract, and study based on the evaluation of clinical trials. An additional evaluation criterion was the publication of articles in peer-reviewed journals.

The search was carried out in for the years January to January Moreover, we selected and examined 45 books and other online materials through Google search, university of Camerino E-database, and the central library of University of Camerino. We retrieved more than articles on telerehabilitation or related topics. We excluded studies or other materials published before from our analysis.

This is because, as Figure 1 shows, the first effective telerehabilitation procedures started in Therefore, the selected articles were published between and We also excluded articles published in nonpeer-reviewed journals, as well as pilot studies, due to the small number of patients investigated. Only English-language articles were selected. Finally, we discarded articles without the terms telerehabilitation, disabling pathologies, telemedicine, or rehabilitation in the title or keywords.

We evaluated the relevant articles with the standard criteria of the Newcastle-Ottawa Scale for assessing the quality of nonrandomized studies in meta-analyses [ 16 ]. Overall study quality was defined as poor score , moderate , or good The score was based on the following filters that could be attributed to a review article: comparability, and desired outcome. In addition, we analyzed various parameters of each article.

The scores depended on these parameters.

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The literature search identified abstracts, of which we analyzed in detail. Among these articles, we excluded in a full-text analysis Figure 2 [ 17 ]. The search analysis showed that, although all these articles matched with the keywords we used, most of them were pilot studies evaluating the response of the system in the real environment in a small sample group of patients. We did not discard these articles because pilot studies are also a valuable source of information, but we considered them as an experience in relation to the patients involved. From our literature analysis, we identified that telerehabilitation was used primarily in cardiac, neurological, and physiotherapy rehabilitation.

Table 1 summarizes data derived from the literature and supplemented by additional information when available [ 1 - 15 , 18 - 29 ]. In chronic cardiac diseases, rehabilitation is one of the main tools used to improve the quality of life of patients, along with a drastic reduction of cardiac risk factors, mainly through lifestyle changes.

Inpatient rehabilitation is in general effective and efficient, whereas in outpatients the quality of rehabilitation is limited. Some studies showed that at least 5 to 30 minutes of aerobic sessions per week reduce cardiac risk factors [ 20 ]. Patients do not join supervised and controlled-phase rehabilitation programs due to scheduling conflicts, difficulties in reaching the training phase, and a reluctance to perform the exercise in a group.

Another study showed that home-based telemonitored cardiac rehabilitation HTCR is a new method of rehabilitation for stable heart failure patients [ 21 ]. For at-home exercise, transtelephonic ECG could be a good substitute for outpatient visits [ 22 ] and probably HTCR produces quality-of-life improvements similar to those obtained by standard outpatient-based cardiac rehabilitation [ 23 ].

In particular, the system has three different training forms: constant load, intervals, and heart rate control. If any limits are exceeded, the patient is alerted through an icon to reduce the load or to immediately abort the exercises. During the experimental phase, no serious events related to heart disease occurred, but some difficulties were observed regarding sensor operation.

The more important advantage of this kind of telerehabilitation system is common to other telehealth systems. Patients can follow their rehabilitation program at a distance eg, at home saving time and money, and avoiding unnecessary travel and discomfort to the patient. The disadvantages are also common among different telerehabilitation systems. In the case of neurological diseases such as brain injury or cognitive problems, the best rehabilitation for patients is to stimulate the brain with adequate environmental interactions.

Probably due to the short history of neurological rehabilitation techniques, neurological telerehabilitation approaches are not clearly defined at this point and have no concrete theoretical bases [ 24 ]. Recent research in human-computer interfaces has improved the effectiveness of virtual reality. Virtual reality consists of simulations through dedicated machines such as personal computers with specific graphical features of a real environment.

The machine could be interfaced with devices such as robotic arms, robotic legs, data gloves, and smart glasses. Such smart devices can be used in a 3-dimensional environment simulation, and they can allow for a greater sense of immersion in the virtual environment [ 10 ]. The first conference on virtual reality applied to medicine was the Medicine Meets Virtual Reality conference [ 25 ].

Advantages of this new type of approach over standard care were discussed. The positive results encouraged the use of this smart technology. Health care treatment closer to the needs of the specific pathologies of the patient improves the quality of life and often decreases the duration of treatment. It has been shown that virtual reality can be used for the assessment and rehabilitation of specific disabilities resulting from brain injury, executive dysfunction, memory impairments, spatial disability, attention deficits, and unilateral visual neglect.

A virtual urban environment for the treatment of 27 patients with moderate and severe brain injuries was developed in which patients needed to navigate in the simulator. However, this study showed no improvement due to the number of repetitions [ 24 ]. Another example of a telerehabilitation system is the Rehab Home framework used to perform rehabilitation in the domestic setting for stroke patients [ 8 ].

The framework consists of instrumented insoles connected wirelessly to a third-generation tablet computer, a server, and a graphic Web interface for medical experts. Rehabilitation progress is automatically analyzed after assessment tests are executed in the tablet computer. Both the systems Rehab Home, virtual urban environment were accepted by patients and doctors because of good results obtained.

Perhaps the systems described above will not be used widely in the future, but they will contribute to improve these approaches in the future, with better cardiac telerehabilitation applications. A telerehabilitation system was applied to 99 poststroke patients to evaluate their quality of life. The authors observed a statistically significant change in both interventions normal and robotic rehabilitations. Actually, both modalities were effective in improving quality of life and depression outcomes for participants at less than 6 months after their stroke.

The goal of this study was to obtain better results for robotic rehabilitation, but the findings obtained did not show significant differences between the 2 groups [ 27 ]. A telerehabilitation project called H-CAD was developed from to H-CAD is a system for patients with multiple sclerosis, stroke, or traumatic brain injury for performing upper limb rehabilitation treatment, at home. A help desk was developed to guide the patients in developing a proper exercise regimen by evaluating the performance periodically.

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Patients had the possibility to interact with doctors at the hospital through a teleconferencing system. The process was carried out in 2 test phases. The first phase was to test the results of the system with volunteers inside a hospital. The second phase was to test the system at home with ad hoc patients. The results were encouraging, and the doctors observed a marked improvement in patients using this system [ 3 ]. More recently, the use of a telerehabilitation approach in the management of patients with depression was studied [ 28 ].

Here, the authors used an Internet-mediated cognitive behavioral therapy iCBT system to treat depression remotely. Unfortunately, considering that depression is an important, modern, and widespread psychiatric disorder, these results were not conclusive [ 28 ]. Eventually an individual treatment design seems to be preferred, and elements of iCBT could be included as a complement when treating depression in primary care. These procedures may be economically important because they could relieve the overall treatment burden of depression.

Musculoskeletal disorders have a high impact on health care provision. A controlled study was conducted to assess the effectiveness of a telerehabilitation approach instead of standard face-to-face practice. A literature review analyzed studies on the validity and reliability of Internet-based physiotherapy assessment for musculoskeletal disorders. Most of the telerehabilitation approaches were valid if they were applied for some physical diseases, except for lumbar spine posture, where the final score was not conclusive.

In fact, results showed that the intervention had effectiveness scores from low to moderate [ 6 ]. Tests confirmed that the intervention had several benefits, particularly in creating a customized physical exercise program for physical rehabilitation [ 7 ]. The application of telemedicine to cardiology, neurology, and rehabilitation is growing fast. For instance, its use in neurology in emergency departments is particularly critical because so many of them do not have a full-time neurologist.

In it was reported that about , patients who had a stroke or symptoms of stroke used telemedicine-based technology in one form or another during treatment or rehabilitation. Telerehabilitation is a young field of telemedicine Figure 1 , and it may cover different areas of medicine [ 2 ]. As a new field, it is still undergoing research and development, and all the applications available are being tested with only a limited number of patients Table 1.

Every system analyzed is a basic one used to check the effectiveness and the responsiveness of patients and doctors to this new approach. It is easily observable that the technology is ready to be used for telerehabilitation. With the support of wireless sensors, microcomputers, and communications systems, it is possible to develop a telerehabilitation system, but further research is required to determine the effectiveness of these systems.

Like every technology, telerehabilitation has some advantages and disadvantages. In terms of advantages, home telerehabilitation systems are cost effective if the intervention is just used to monitor or evaluate patients during corrective therapy [ 14 , 29 ]. The possibility to stay in touch with telematic technologies allows patients with serious pathologies, such as severe cognitive deficits, to perform physiotherapy at home without having to make tiring journeys.

In terms of disadvantages, a problem could be the loss of human contact face-to-face interaction with the doctor. Moreover, for each patient, system operators are required to optimize the teletherapy according to the type of disease, and sometimes this is not possible due to high costs. In conclusion, this analysis has shown that telerehabilitation is a new and interesting field but, unfortunately at present, there are no standard procedures or protocols, and different telerehabilitation facilities are being used for pilot studies only.

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Herein, we suggest the need for further research to improve the electronic equipment and devices, and to make their application as flexible as possible. This approach should significantly increase the reliability and effectiveness of telerehabilitation equipment to treat specific patient problems. Furthermore, in this context, feedback from patients may be important to update rehabilitation techniques to improve the quality of the rehabilitation itself.

On the other hand, an important aspect of the future success of telerehabilitation involves proper training of people involved in these new forms of intervention, which may lead to more effective rehabilitation. This study was supported by a grant from Triskel Resort.

Conflicts of Interest: None declared. National Center for Biotechnology Information , U. Published online Jul Author information Article notes Copyright and License information Disclaimer. Corresponding author. Corresponding Author: Alessandro Peretti ti. This article has been cited by other articles in PMC. Abstract Background Telemedicine applications have been increasing due to the development of new computer science technologies and of more advanced telemedical devices. Objective The objective of this review is to provide a detailed overview of the rehabilitation techniques for remote sites telerehabilitation and their fields of application, with analysis of the benefits and the drawbacks related to use.

Results Telerehabilitation can be considered as a branch of telemedicine. Conclusions This review evaluated different application fields of telerehabilitation, highlighting its benefits and drawbacks. Keywords: telerehabilitation, rehabilitation, telemedicine, health care, remote rehabilitation assistance. Introduction In the last few years, telemedicine applications have been increasing due to the development of new computer science technologies and of more advanced telemedical devices.

Open in a separate window. Figure 1. Number of patients treated from to through telerehabilitation techniques. Exclusion Criteria We excluded studies or other materials published before from our analysis. Quality Assessment We evaluated the relevant articles with the standard criteria of the Newcastle-Ottawa Scale for assessing the quality of nonrandomized studies in meta-analyses [ 16 ].

Results Evaluation Outcomes The literature search identified abstracts, of which we analyzed in detail. Figure 2. Table 1 Characteristics of studies on telerehabilitation reviewed. First author, date, reference Type of article Rehabilitation area Sample size Article key points Positive aspects Barriers and limitations Ackerman, [ 1 ] Original research Multiple rehabilitation areas Literature review Next-generation telehealth tools Devices are available at home; electronic health record available for each person; interaction of multiple systems.

People and technological systems are not ready data flow and incompatibility between telerehabilitation systems. Rogante, [ 2 ] Review article Multiple rehabilitation areas Literature review Overview of telerehabilitation literature Provides some techniques at a distance. Health care providers are not ready; comprehensive studies are lacking. Zampolini, [ 3 ] Review article Multiple rehabilitation areas Literature review Overview of telerehabilitation literature and a study The possibilities of using telerehabilitation as standard in the future.

Technologies, patients, and health care providers are not ready. Carey, [ 4 ] Original research Physiotherapy Literature review Cortical reorganization after stroke Telerehabilitation may be effective in improving performance in patients with chronic stroke. No clear advantage produced over the same amount of practice of random movements. Parmanto, [ 5 ] Review article Multiple rehabilitation areas Literature review Telerehabilitation from informatics perspective Information technology and telerehabilitation are the future.

Health care providers are not ready to manage an everyday telerehabilitation approach.

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Mani, [ 6 ] Review article Physiotherapy Literature review Telerehabilitation in musculoskeletal disorders Telerehabilitation-based physiotherapy assessment is technically feasible. Telerehabilitation-based physiotherapy assessment was not feasible or reliable for lumbar spine posture, orthopedic special tests, neurodynamic tests, and scar assessment. Gal, [ 7 ] Original research Physiotherapy Literature review Kinect-based system in physiotherapy Kinect can greatly help people in rehabilitation.

Not present. Jagos, [ 8 ] Clinical trial Cardiac rehabilitation 5 patients Rehabilitation after stroke The system used could be used for further analysis. Keshner, [ 9 ] Original research Multiple rehabilitation areas Literature review VR a as a treatment intervention VR should be used more in the future. People are not ready. Larson, [ 10 ] Review article Multiple rehabilitation areas Literature review VR treatment VR is effectively used for telerehabilitation. Further studies are needed to optimize the techniques. Kenyon, [ 11 ] Review article Multiple rehabilitation areas Literature review VR treatment The virtual environment can be a valuable tool for therapeutic interventions that require adaptation to complex, multimodal environments.

Lewis, [ 12 ] Review article Multiple rehabilitation areas Literature review VR treatment and human factors VR has many potentialities in health care. Some users experienced adverse effects during and after exposure to VR environments ocular problems, disorientation and balance disturbances, and nausea. Burdea, [ 13 ] Case study Physiotherapy and neurological rehabilitation Literature review Cerebral palsy motor control improvement Game-based robotic training of the ankle benefits gait in children with cerebral palsy. Additional studies are needed to quantify the level of benefit and for comparing different approaches.

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

When computers with large memories became available around , researchers from all three traditions began to build knowledge into AI applications. By the s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception , robotics , learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems. This includes embodied , situated , behavior-based , and nouvelle AI.

Researchers from the related field of robotics , such as Rodney Brooks , rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive. This coincided with the development of the embodied mind thesis in the related field of cognitive science : the idea that aspects of the body such as movement, perception and visualization are required for higher intelligence. Within developmental robotics , developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms active learning, maturation, motor synergies, etc.

Interest in neural networks and " connectionism " was revived by David Rumelhart and others in the middle of the s. Other soft computing approaches to AI include fuzzy systems , Grey system theory , evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence. 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 real-world results.

However, around the s, 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. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific.

Nowadays results of experiments are often rigorously measurable, and are sometimes with difficulty reproducible. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence. AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Many problems in AI can be solved in theory by intelligently searching through many possible solutions: [] Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions , where each step is the application of an inference rule.

Simple exhaustive searches [] are rarely sufficient for most real-world problems: the search space the number of places to search quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use " heuristics " or "rules of thumb" that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps.

In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal called " pruning the search tree ". Heuristics supply the program with a "best guess" for the path on which the solution lies. A very different kind of search came to prominence in the s, 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. 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. Two popular swarm algorithms used in search are particle swarm optimization inspired by bird flocking and ant colony optimization inspired by ant trails. Logic [] is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning [] and inductive logic programming is a method for learning.

Several different forms of logic are used in AI research. Propositional logic [] involves truth functions such as "or" and "not". First-order logic [] adds quantifiers and predicates , and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a "degree of truth" between 0 and 1 to vague statements such as "Alice is old" or rich, or tall, or hungry that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train's brake pressure"; these vague rules can then be numerically refined within the system.

Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences. Default logics , non-monotonic logics and circumscription [96] are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge , such as: description logics ; [84] situation calculus , event calculus and fluent calculus for representing events and time ; [85] causal calculus ; [86] belief calculus; [] and modal logics.

Overall, qualitative 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. 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 [] are a very general tool that can be used for a large number of problems: reasoning using the Bayesian inference algorithm , [] learning using the expectation-maximization algorithm , [f] [] planning using decision networks [] and perception using dynamic Bayesian networks. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other "loops" undirected cycles can require a sophisticated method such as Markov chain Monte Carlo , which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities.

Bayesian networks are used on Xbox Live to rate and match players; wins and losses are "evidence" of how good a player is. AdSense uses a Bayesian network with over million edges to learn which ads to serve. A key concept from the science of economics is " utility ": a measure of how valuable something is to an intelligent agent.

Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory , decision analysis , [] 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, or neural nets, were inspired by the architecture of neurons in the human brain. A simple "neuron" N accepts input from multiple other neurons, each of which, when activated or "fired" , cast a weighted "vote" for or against whether neuron N should itself activate.

Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm dubbed " fire together, wire together " is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The net forms "concepts" that are distributed among a subnetwork of shared [j] neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot".

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

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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.

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.

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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. 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.

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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.

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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. 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 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. 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".

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 not only won at the game show Jeopardy! 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.

Programs like Kasisto and Moneystream are using AI in financial services. Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place. 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. 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. 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. 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.

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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 5 July 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. 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. 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. Abductive reasoning A. Rising Artificial intelligence arms race Artificial intelligence portal 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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