Applications of artificial intelligence

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Artificial intelligence
Major goals
Reasoning
Knowledge representation
Automated planning and scheduling
Machine learning
Natural language processing
Computer vision
Robotics
Artificial general intelligence
Traditional approaches
Search algorithms
Logic
Modern approaches
Neural networks
Deep learning
Recurrent neural networks
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

Artificial intelligence has been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, remote sensing, scientific discovery and toys. However, due to the AI effect, many AI applications are not perceived as AI. "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore," Nick Bostrom, a noted philosopher, reports.[1] "Many thousands of AI applications are deeply embedded in the infrastructure of every industry."[2] In the late 90s and early 21st century, AI technology became widely used as elements of larger systems,[2][3] but the field is rarely credited for these successes. It continues to develop in numerous fields today including:

Computer science[edit]

AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered a part of AI. (See AI effect). According to Russell & Norvig (2003, p. 15), all of the following were originally developed in AI laboratories: time sharing, interactive interpreters, graphical user interfaces and the computer mouse, rapid development environments, the linked list data structure, automatic storage management, symbolic programming, functional programming, dynamic programming and object-oriented programming.

Finance[edit]

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation.

Use of AI in banking can be tracked back to 1987 when Security Pacific National Bank in USA set-up a Fraud Prevention Task force to counter the unauthorised use of debit cards. Apps like Kasisito and Moneystream are using AI in financial services

Banks use artificial intelligence systems to organize operations, maintain book-keeping, invest in stocks, and manage properties. For example, Kensho is a computer system that is used to analyze how well portfolios perform and predict changes in the market. AI can react to changes overnight or when business is not taking place.[4] In August 2001, robots beat humans in a simulated financial trading competition.[5]

Also, systems are being developed, like Arria, to translate complex data into simple and personable language.[4]

There are also wallets, like Wallet.AI that monitor an individual's spending habits and provides ways to improve them.[6]

AI has also reduced fraud and crime by monitoring behavioral patterns of users for any changes or anomalies.[6]

Hospitals and medicine[edit]

Artificial neural networks are used as clinical decision support systems for medical diagnosis, such as in Concept Processing technology in EMR software.

Other tasks in medicine that can potentially be performed by artificial intelligence and are beginning to be developed include:

  • Computer-aided interpretation of medical images. Such systems help scan digital images, e.g. from computed tomography, for typical appearances and to highlight conspicuous sections, such as possible diseases. A typical application is the detection of a tumor.
  • Heart sound analysis[7]
  • Watson project is another use of AI in this field,a Q/A program that suggest for doctor's of cancer patients.
  • Companion robots for the care of the elderly [8]
  • mining medical records to provide more useful information
  • design treatment plans
  • assist in repetitive jobs including medication management
  • provide consultations
  • drug creation[9]

Currently, there are over 90 AI startups in the health industry working in these fields.[10]

Heavy industry[edit]

Robots have become common in many industries and are often given jobs that are considered dangerous to humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading.

In 2014, China, Japan, the United States, the Republic of Korea and Germany together amounted to 70% of the total sales volume of robots. In the automotive industry, a sector with particularly high degree of automation, Japan had the highest density of industrial robots in the world: 1,414 per 10,000 employees.[11]

Online and telephone customer service[edit]

An automated online assistant providing customer service on a web page.

Artificial intelligence is implemented in automated online assistants that can be seen as avatars on web pages.[12] It can avail for enterprises to reduce their operation and training cost.[12] A major underlying technology to such systems is natural language processing.[12] Pypestream uses automated customer service for its mobile application designed to streamline communication with customers.[13]

Currently, major companies are investing in AI to handle difficult customer in the future. Google's most recent development analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately.[14]

Companies have been working on different aspects of customer service to improve this aspect of a company.

Digital Genius, an AI start-up, researches the database of information (from past conversations and frequently asked questions) more efficiently and provide prompts to agents to help them resolve queries more efficiently.

IPSoft is creating technology with emotional intelligence to adapt the customer's interaction. The response is linked to the customer's tone, with the objective of being able to show empathy. Another element IPSoft is developing is the ability to adapt to different tones or languages.

Inbenta’s is focused on developing natural language. In other words, on understanding the meaning behind what someone is asking and not just looking at the words used, using context and natural language processing. One customer service element Ibenta has already achieved is its ability to respond in bulk to email queries.

Transportation[edit]

Many companies have been progressing quickly in this field with AI.[15]

Fuzzy logic controllers have been developed for automatic gearboxes in automobiles. For example, the 2006 Audi TT, VW Touareg[citation needed] and VW Caravell feature the DSP transmission which utilizes Fuzzy Logic. A number of Škoda variants (Škoda Fabia) also currently include a Fuzzy Logic-based controller.

AI in transportation is expected to provide safe, efficient, and reliable transportation while minimizing the impact on the environment and communities. The major challenge to developing this AI is the fact that transportation systems are inherently complex systems involving a very large number of components and different parties, each having different and often conflicting objectives.[16]

Telecommunications maintenance[edit]

Many telecommunications companies make use of heuristic search in the management of their workforces, for example BT Group has deployed heuristic search[17] in a scheduling application that provides the work schedules of 20,000 engineers.

Toys and games[edit]

The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of Artificial Intelligence, specifically in the form of Tamagotchis and Giga Pets, iPod Touch, the Internet, and the first widely released robot, Furby. A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy.

Companies like Mattel have been creating an assortment of AI-enabled toys for kids as young as age three. Using proprietary AI engines and speech recognition tools, they are able to understand conversations, give intelligent responses and learn quickly.[18]

AI has also been applied to video games, for example video game bots, which are designed to stand in as opponents where humans aren't available or desired; or the AI Director from Left 4 Dead, which decides where enemies spawn and how maps are laid out to be more or less challenging at various points of play.[19]

Music[edit]

While the evolution of music has always been affected by technology, artificial intelligence has enabled, through scientific advances, to emulate, at some extent, human-like composition.

Among notable early efforts, David Cope created an AI called Emily Howell that managed to become well known in the field of Algorithmic Computer Music.[20] The algorithm behind Emily Howell is registered as a US patent.[21]

Other endeavours, like AIVA (Artificial Intelligence Virtual Artist), focus on composing symphonic music, mainly classical music for film scores.[22] It achieved a world first by becoming the first virtual composer to be recognized by a musical professional association.[23]

Artificial intelligences can even produce music usable in a medical setting, with Melomics’s effort to use computer-generated music for stress and pain relief.[24]

Moreover, initiatives such as Google Magenta, conducted by the Google Brain team, want to find out if an artificial intelligence can be capable of creating compelling art.[25]

At Sony CSL Research Laboratory, their Flow Machines software has created pop songs by learning music styles from a huge database of songs. By analyzing unique combinations of styles and optimizing techniques, it can compose in any style.

Aviation[edit]

The Air Operations Division (AOD) uses AI for the rule based expert systems. The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.[26]

The use of artificial intelligence in simulators is proving to be very useful for the AOD. Airplane simulators are using artificial intelligence in order to process the data taken from simulated flights. Other than simulated flying, there is also simulated aircraft warfare. The computers are able to come up with the best success scenarios in these situations. The computers can also create strategies based on the placement, size, speed and strength of the forces and counter forces. Pilots may be given assistance in the air during combat by computers. The artificial intelligent programs can sort the information and provide the pilot with the best possible maneuvers, not to mention getting rid of certain maneuvers that would be impossible for a human being to perform. Multiple aircraft are needed to get good approximations for some calculations so computer simulated pilots are used to gather data.[27] These computer simulated pilots are also used to train future air traffic controllers.

The system used by the AOD in order to measure performance was the Interactive Fault Diagnosis and Isolation System, or IFDIS. It is a rule based expert system put together by collecting information from TF-30 documents and the expert advice from mechanics that work on the TF-30. This system was designed to be used for the development of the TF-30 for the RAAF F-111C. The performance system was also used to replace specialized workers. The system allowed the regular workers to communicate with the system and avoid mistakes, miscalculations, or having to speak to one of the specialized workers.

The AOD also uses artificial intelligence in speech recognition software. The air traffic controllers are giving directions to the artificial pilots and the AOD wants to the pilots to respond to the ATC's with simple responses. The programs that incorporate the speech software must be trained, which means they use neural networks. The program used, the Verbex 7000, is still a very early program that has plenty of room for improvement. The improvements are imperative because ATCs use very specific dialog and the software needs to be able to communicate correctly and promptly every time.

The Artificial Intelligence supported Design of Aircraft,[28] or AIDA, is used to help designers in the process of creating conceptual designs of aircraft. This program allows the designers to focus more on the design itself and less on the design process. The software also allows the user to focus less on the software tools. The AIDA uses rule based systems to compute its data. This is a diagram of the arrangement of the AIDA modules. Although simple, the program is proving effective.

In 2003, NASA's Dryden Flight Research Center, and many other companies, created software that could enable a damaged aircraft to continue flight until a safe landing zone can be reached.[29] The software compensates for all the damaged components by relying on the undamaged components. The neural network used in the software proved to be effective and marked a triumph for artificial intelligence.

The Integrated Vehicle Health Management system, also used by NASA, on board an aircraft must process and interpret data taken from the various sensors on the aircraft. The system needs to be able to determine the structural integrity of the aircraft. The system also needs to implement protocols in case of any damage taken the vehicle.

News, publishing and writing[edit]

The company Narrative Science makes computer generated news and reports commercially available, including summarizing team sporting events based on statistical data from the game in English. It also creates financial reports and real estate analyses.[30]

The company Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football.[31] The company is projected to generate one billion stories in 2014, up from 350 million in 2013.[32]

Another company, called Yseop, uses artificial intelligence to turn structured data into intelligent comments and recommendations in natural language. Yseop is able to write financial reports, executive summaries, personalized sales or marketing documents and more at a speed of thousands of pages per second and in multiple languages including English, Spanish, French & German.[33]

Boomtrain’s is another example of AI that is designed to learn how to best engage each individual reader with the exact articles — sent through the right channel at the right time — that will be most relevant to the reader. It’s like hiring a personal editor for each individual reader to curate the perfect reading experience.

There is also the possibility that AI will write work in the future. In 2016, a Japanese AI co-wrote a short story and almost won a literary prize.[34]

Other[edit]

Various tools of artificial intelligence are also being widely deployed in homeland security, speech and text recognition, data mining, and e-mail spam filtering. Applications are also being developed for gesture recognition (understanding of sign language by machines), individual voice recognition, global voice recognition (from a variety of people in a noisy room), facial expression recognition for interpretation of emotion and non verbal cues. Other applications are robot navigation, obstacle avoidance, and object recognition.[citation needed]

List of applications[edit]

Typical problems to which AI methods are applied
Other fields in which AI methods are implemented

See also[edit]

Notes[edit]

  1. ^ AI set to exceed human brain power CNN.com (July 26, 2006)
  2. ^ a b Kurzweil 2005, p. 264.
  3. ^ NRC 1999, "Artificial Intelligence in the 90s".
  4. ^ a b Eleanor, O' Neill,. "Accounting, automation and AI". www.icas.com. Retrieved 2016-11-18. 
  5. ^ Robots Beat Humans in Trading Battle. BBC.com (August 8, 2001)
  6. ^ a b "CTO Corner: Artificial Intelligence Use in Financial Services - Financial Services Roundtable". Financial Services Roundtable. 2015-04-02. Retrieved 2016-11-18. 
  7. ^ Reed, T. R.; Reed, N. E.; Fritzson, P. (2004). "Heart sound analysis for symptom detection and computer-aided diagnosis". Simulation Modelling Practice and Theory. 12 (2): 129. doi:10.1016/j.simpat.2003.11.005. 
  8. ^ Yorita, A.; Kubota, N. (2011-03-01). "Cognitive Development in Partner Robots for Information Support to Elderly People". IEEE Transactions on Autonomous Mental Development. 3 (1): 64–73. doi:10.1109/TAMD.2011.2105868. ISSN 1943-0604. 
  9. ^ "Artificial Intelligence Will Redesign Healthcare - The Medical Futurist". The Medical Futurist. 2016-08-04. Retrieved 2016-11-18. 
  10. ^ "From Virtual Nurses To Drug Discovery: 90+ Artificial Intelligence Startups In Healthcare". CB Insights - Blog. 2016-08-31. Retrieved 2016-11-18. 
  11. ^ "World Robotics 2015 Industrial Robots". International Federation of Robotics. Archived from the original on March 27, 2016. Retrieved 27 March 2016. 
  12. ^ a b c Implementing an online help desk system based on conversational agent Authors: Alisa Kongthon, Chatchawal Sangkeettrakarn, Sarawoot Kongyoung and Choochart Haruechaiyasak. Published by ACM 2009 Article, Bibliometrics Data Bibliometrics. Published in: Proceeding, MEDES '09 Proceedings of the International Conference on Management of Emergent Digital EcoSystems, ACM New York, NY, USA. ISBN 978-1-60558-829-2, doi:10.1145/1643823.1643908
  13. ^ Sara Ashley O'Brien (January 12, 2016). "Is this app the call center of the future?". CNN. Retrieved September 26, 2016. 
  14. ^ jackclarkSF, Jack Clark (2016-07-20). "New Google AI Brings Automation to Customer Service". Bloomberg.com. Retrieved 2016-11-18. 
  15. ^ "Transportation | One Hundred Year Study on Artificial Intelligence (AI100)". ai100.stanford.edu. Retrieved 2016-11-18. 
  16. ^ Meyer, Michael D. (January 2007). "Artificial Intelligence in Transportation Information for Application" (PDF). Transportation Research Circular. 
  17. ^ Success Stories Archived October 4, 2011, at the Wayback Machine..
  18. ^ "How artificial intelligence is moving from the lab to your kid's playroom". Washington Post. Retrieved 2016-11-18. 
  19. ^ "The Director". Left 4 Dead Wiki. Retrieved 2016-11-18. 
  20. ^ Cheng, Jacqui (30 September 2009). "Virtual composer makes beautiful music—and stirs controversy". Ars Technica. 
  21. ^ US Patent #7696426 https://www.google.com/patents/US7696426
  22. ^ Hick, Thierry (11 October 2016). "La musique classique recomposée". Luxemburger Wort. 
  23. ^ SACEM Database, https://repertoire.sacem.fr/resultats?filters=parties&query=aiva&nbWorks=20)
  24. ^ Requena, G; Sanchez, C; Corzo-Higueras, JL; Reyes-Alvarado, S; Rivas-Ruiz, F; Vico, F; Raglio, A (2014). "Melomics music medicine (M3) to lessen pain perception during pediatric prick test procedure". Pediatric Allergy and Immunology. doi:10.1111/pai.12263. 
  25. ^ Souppouris, Aaron (23 May 2016). "Google's 'Magenta' project will see if AIs can truly make art". Engadget. 
  26. ^ "AI bests Air Force combat tactics experts in simulated dogfights". Ars Technica. Retrieved 2016-11-18. 
  27. ^ Jones, Randolph M.; Laird, John E.; Nielsen, Paul E.; Coulter, Karen J.; Kenny, Patrick; Koss, Frank V. (1999-03-15). "Automated Intelligent Pilots for Combat Flight Simulation". AI Magazine. 20 (1): 27. ISSN 0738-4602. 
  28. ^ AIDA Homepage. Kbs.twi.tudelft.nl (April 17, 1997). Retrieved on 2013-07-21.
  29. ^ The Story of Self-Repairing Flight Control Systems. NASA Dryden. (April 2003). Retrieved on 2016-08-25.
  30. ^ business intelligence solutions Archived November 3, 2011, at the Wayback Machine.. Narrative Science. Retrieved on 2013-07-21.
  31. ^ Eule, Alexander. "Big Data and Yahoo's Quest for Mass Personalization". Barron's. 
  32. ^ Kirkland, Sam. "'Robot' to write 1 billion stories in 2014 — but will you know it when you see it?". Poynter. 
  33. ^ http://yseop.com/EN/solutions.html
  34. ^ "A Japanese AI program just wrote a short novel, and it almost won a literary prize". Digital Trends. 2016-03-23. Retrieved 2016-11-18. 

External links[edit]

References[edit]

  • Russell, Stuart J.; Norvig, Peter (2003). Artificial Intelligence: A Modern Approach (2nd ed.). Upper Saddle River, New Jersey: Prentice Hall. ISBN 0-13-790395-2 
  • Kurzweil, Ray (2005). The Singularity is Near: When Humans Transcend Biology. New York: Viking. ISBN 978-0-670-03384-3 
  • National Research Council (1999). "Developments in Artificial Intelligence". Funding a Revolution: Government Support for Computing Research. National Academy Press. ISBN 0-309-06278-0. OCLC 246584055. 
  • Moghaddam, M. J., M. R. Soleymani, and M. A. Farsi. "Sequence planning for stamping operations in progressive dies." Journal of Intelligent Manufacturing(2013): 1-11.