Applications of artificial intelligence

AI for Good is an ITU initiative supporting institutions employing AI to tackle some of the world's greatest economic and social challenges. For example, the University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address socially relevant problems such as homelessness. At Stanford, researchers are using AI to analyze satellite images to identify which areas have the highest poverty levels.[1]

In agriculture new AI advancements show improvements in gaining yield and to increase the research and development of growing crops. New artificial intelligence now predicts the time it takes for a crop like a tomato to be ripe and ready for picking thus increasing efficiency of farming.[2] These advances go on including Crop and Soil Monitoring, Agricultural Robots, and Predictive Analytics. Crop and soil monitoring uses new algorithms and data collected on the field to manage and track the health of crops making it easier and more sustainable for the farmers.[3]

More specializations of AI in agriculture is one such as greenhouse automation, simulation, modeling, and optimization techniques.

Due to the increase in population and the growth of demand for food in the future, there will need to be at least a 70% increase in yield from agriculture to sustain this new demand. More and more of the public perceives that the adaption of these new techniques and the use of Artificial intelligence will help reach that goal.[4]

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.[5]

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.[6] 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 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,[7] 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.[8] 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.[9]

Haitham Baomar and Peter Bentley are leading a team from the University College of London to develop an artificial intelligence based Intelligent Autopilot System (IAS) designed to teach an autopilot system to behave like a highly experienced pilot who is faced with an emergency situation such as severe weather, turbulence, or system failure.[10] Educating the autopilot relies on the concept of supervised machine learning “which treats the young autopilot as a human apprentice going to a flying school”.[10] The autopilot records the actions of the human pilot generating learning models using artificial neural networks.[10] The autopilot is then given full control and observed by the pilot as it executes the training exercise.[10]

The Intelligent Autopilot System combines the principles of Apprenticeship Learning and Behavioural Cloning whereby the autopilot observes the low-level actions required to maneuver the airplane and high-level strategy used to apply those actions.[11] IAS implementation employs three phases; pilot data collection, training, and autonomous control.[11] Baomar and Bentley's goal is to create a more autonomous autopilot to assist pilots in responding to emergency situations.[11]

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 application development environments, the linked list data structure, automatic storage management, symbolic programming, functional programming, dynamic programming and object-oriented programming.

AI can be used to potentially determine the developer of anonymous binaries.

AI can be used to create other AI. For example, around November 2017, Google's AutoML project to evolve new neural net topologies created NASNet, a system optimized for ImageNet and POCO F1. According to Google, NASNet's performance exceeded all previously published ImageNet performance.[12]

In June 2016, a research team from the visual computing group of the Technical University of Munich and from Stanford University developed Face2Face,[13] a program which animates the face of a target person, transposing the facial expressions of an exterior source. The technology has been demonstrated animating the lips of people including Barack Obama and Vladimir Putin. Since then, other methods have been demonstrated based on deep neural network, from which the name "deepfake" was taken.

In September 2018, the U.S. Senator Mark Warner proposed to penalize social media companies that allow sharing of deepfake documents on their platform.[14]

Vincent Nozick, a researcher from the Institut Gaspard Monge, found a way to detect rigged documents by analyzing the movements of the eyelid. The DARPA (a research group associated with the U.S. Department of Defense) has given 68 million dollars to work on deepfake detection. In Europe, the Horizon 2020 program financed InVid, software designed to help journalists to detect fake documents.

Deepfakes can be used for comedic purposes, but are better known for being used for fake news and hoaxes. Audio deepfakes, and AI software capable of detecting deepfakes and cloning human voices after 5 seconds of listening time also exist.[15][16][17][18][19][20]

AI tutors could allow for students to get extra, one-on-one help. They could also reduce anxiety and stress for some students, that may be caused by tutor labs or human tutors.[21] In future classrooms, ambient informatics can play a beneficial role. Ambient informatics is the idea that information is everywhere in the environment and that technologies automatically adjust to your personal preferences.[22] Study devices could be able to create lessons, problems, and games to tailor to the specific student's needs, and give immediate feedback.

But AI can also create a disadvantageous environment with revenge effects, if technology is inhibiting society from moving forward and causing negative, unintended effects on society.[23] An example of a revenge effect is that the extended use of technology may hinder students’ ability to focus and stay on task instead of helping them learn and grow.[24] Also, AI has been known to lead to the loss of both human agency and simultaneity.[22]

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 that 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.[38]

X-ray of a hand, with automatic calculation of bone age by a computer software

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[39]
• Companion robots for the care of the elderly[40]
• Mining medical records to provide more useful information.
• Design treatment plans.
• Assist in repetitive jobs including medication management.
• Provide consultations.
• Drug creation[41]
• Using avatars in place of patients for clinical training[42]
• Predict the likelihood of death from surgical procedures
• Predict HIV progression

There are over 90 AI startups in the health industry working in these fields.[43]

IDx's first solution, IDx-DR, is the first autonomous AI-based diagnostic system authorized for commercialization by the FDA.[44]

Another application of AI is in the human resources and recruiting space. There are three ways AI is being used by human resources and recruiting professionals: to screen resumes and rank candidates according to their level of qualification, to predict candidate success in given roles through job matching platforms, and rolling out recruiting chatbots that can automate repetitive communication tasks. Typically, resume screening involves a recruiter or other HR professional scanning through a database of resumes.

The job market has seen a notable change due to artificial intelligence implementation. It has simplified the process for both recruiters and job seekers (i.e., Google for Jobs and applying online). According to Raj Mukherjee from Indeed.com, 65% of people launch a job search again within 91 days of being hired. AI-powered engine streamlines the complexity of job hunting by operating information on job skills, salaries, and user tendencies, matching people to the most relevant positions. Machine intelligence calculates what wages would be appropriate for a particular job, pulls and highlights resume information for recruiters using natural language processing, which extracts relevant words and phrases from text using specialized software. Another application is an AI resume builder which requires 5 minutes to compile a CV as opposed to spending hours doing the same job. In the AI age chatbots assist website visitors and solve daily workflows. Revolutionary AI tools complement people's skills and allow HR managers to focus on tasks of higher priority. However, Artificial Intelligence's impact on jobs research suggests that by 2030 intelligent agents and robots can eliminate 30% of the world's human labor. Moreover, the research proves automation will displace between 400 and 800 million employees. Glassdoor's research report states that recruiting and HR are expected to see much broader adoption of AI in job market 2018 and beyond.[45][46]

Some AI applications are geared towards the analysis of audiovisual media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision, which is a major application area of AI.

Typical use case scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for recognizing relevant scenes, objects or faces. The motivation for using AI-based media analysis can be — among other things — the facilitation of media search, the creation of a set of descriptive keywords for a media item, media content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for the placement of relevant advertisements.

Media analysis AI companies often provide their services over a REST API that enables machine-based automatic access to the technology and allows machine-reading of the results. For example, IBM, Microsoft, and Amazon allow access to their media recognition technology by using RESTful APIs.

The United States and other nations are developing AI applications for a range of military functions.[47] The main military applications of Artificial Intelligence and Machine Learning are to enhance C2, Communications, Sensors, Integration and Interoperability.[48] AI research is underway in the fields of intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and in a variety of semiautonomous and autonomous vehicles.[47] Artificial Intelligence technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T).[48] AI has been incorporated into military operations in Iraq and Syria.[47]

Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.[49][50] Military drones capable of autonomous action are widely considered a useful asset.[51] Many artificial intelligence researchers seek to distance themselves from military applications of AI.[52]

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.[53] The algorithm behind Emily Howell is registered as a US patent.[54]

The AI Iamus created 2012 the first complete classical album fully composed by a computer.

Other endeavours, like AIVA (Artificial Intelligence Virtual Artist), focus on composing symphonic music, mainly classical music for film scores.[55] It achieved a world first by becoming the first virtual composer to be recognized by a musical professional association.[56]

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.[57]

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.[58]

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.

Another artificial intelligence musical composition project, The Watson Beat, written by IBM Research, doesn't need a huge database of music like the Google Magenta and Flow Machines projects since it uses Reinforcement Learning and Deep Belief Networks to compose music on a simple seed input melody and a select style. Since the software has been open sourced[59] musicians, such as Taryn Southern[60] have been collaborating with the project to create music.

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.[61] Similarly, the company Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football.[62] The company is projected to generate one billion stories in 2014, up from 350 million in 2013.[63] The organisation OpenAI has also created an AI capable of writing text.[64]

Echobox is a software company that helps publishers increase traffic by 'intelligently' posting articles on social media platforms such as Facebook and Twitter.[65] By analysing large amounts of data, it learns how specific audiences respond to different articles at different times of the day. It then chooses the best stories to post and the best times to post them. It uses both historical and real-time data to understand to what has worked well in the past as well as what is currently trending on the web.[66]

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.[67]

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.

IRIS.TV is helping media companies with its AI-powered video personalization and programming platform. It allows publishers and content owners to surface contextually relevant content to audiences based on consumer viewing patterns.[68]

Beyond automation of writing tasks given data input, AI has shown significant potential for computers to engage in higher-level creative work. AI Storytelling has been an active field of research since James Meehan's development of TALESPIN, which made up stories similar to the fables of Aesop. The program would start with a set of characters who wanted to achieve certain goals, with the story as a narration of the characters’ attempts at executing plans to satisfy these goals.[69] Since Meehan, other researchers have worked on AI Storytelling using similar or different approaches. Mark Riedl and Vadim Bulitko argued that the essence of storytelling was an experience management problem, or "how to balance the need for a coherent story progression with user agency, which is often at odds."[70]

While most research on AI storytelling has focused on story generation (e.g. character and plot), there has also been significant investigation in story communication. In 2002, researchers at North Carolina State University developed an architectural framework for narrative prose generation. Their particular implementation was able faithfully reproduced text variety and complexity of a number of stories, such as red riding hood, with human-like adroitness.[71] This particular field continues to gain interest. In 2016, a Japanese AI co-wrote a short story and almost won a literary prize.[72]

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.[73] It can avail for enterprises to reduce their operation and training cost.[73] A major underlying technology to such systems is natural language processing.[73] Pypestream uses automated customer service for its mobile application designed to streamline communication with customers.[74]

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.[75]

Power electronics converters are an enabling technology for renewable energy, energy storage, electric vehicles and high-voltage direct current transmission systems within the electrical grid. These converters are prone to failures and such failures can cause downtimes that may require costly maintenance or even have catastrophic consequences in mission critical applications. Researchers are using AI to do the automated design process for reliable power electronics converters, by calculating exact design parameters that ensure desired lifetime of the converter under specified mission profile.[76]

Artificial Intelligence has been combined with many sensor technologies, such as Digital Spectrometry by IdeaCuria Inc.[77][78] which enables many applications such as at home water quality monitoring.

Many telecommunications companies make use of heuristic search in the management of their workforces, for example BT Group has deployed heuristic search[79] in a scheduling application that provides the work schedules of 20,000 engineers.

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.[80]

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.

Fuzzy logic controllers have been developed for automatic gearboxes in automobiles. For example, the 2006 Audi TT, VW Touareg 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.

Today's cars now have AI-based driver-assist features such as self-parking and advanced cruise controls. AI has been used to optimize traffic management applications, which in turn reduces wait times, energy use, and emissions by as much as 25 percent.[1] In the future, fully autonomous cars will be developed. 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.[81] Due to this high degree of complexity of the transportation, and in particular the automotive, application, it is in most cases not possible to train an AI algorithm in a real-world driving environment. To overcome the challenge of training neural networks for automated driving, methodologies based on virtual development resp. testing toolchains[82] have been proposed.

Studies related to Wikipedia have been using artificial intelligence to support various operations. Two of the most important areas are automatic detection of vandalism [83][84] and data quality assessment in Wikipedia.[85][86]

The team at the Wikimedia Foundation released a model that is designed to detect vandalism, spam, and personal attack.[87] This model can also help students write better Wikipedia articles.[88]

Typical problems to which AI methods are applied

Other fields in which AI methods are implemented

• Applications of artificial intelligence to legal informatics
• Applications of deep learning
• Applications of machine learning
• List of artificial intelligence projects
• Progress in artificial intelligence

• Russell, Stuart J.; Norvig, Peter (2003). Artificial Intelligence: A Modern Approach (2nd ed.). Upper Saddle River, New Jersey: Prentice Hall. ISBN 978-0-13-790395-5.CS1 maint: ref=harv (link)
• Kaplan, A.M.; Haenlein, M. (2018). "Siri, Siri in my Hand, who's the Fairest in the Land? On the Interpretations, Illustrations and Implications of Artificial Intelligence". Business Horizons. 62 (1): 15–25. doi:10.1016/j.bushor.2018.08.004.
• Kurzweil, Ray (2005). The Singularity is Near: When Humans Transcend Biology. New York: Viking. ISBN 978-0-670-03384-3.CS1 maint: ref=harv (link)
• National Research Council (1999). "Developments in Artificial Intelligence". Funding a Revolution: Government Support for Computing Research. National Academy Press. ISBN 978-0-309-06278-7. OCLC 246584055.
• Moghaddam, M. J.; Soleymani, M. R.; Farsi, M. A. (2015). "Sequence planning for stamping operations in progressive dies". Journal of Intelligent Manufacturing. 26 (2): 347–357. doi:10.1007/s10845-013-0788-0.
• Felten, Ed (May 3, 2016). "Preparing for the Future of Artificial Intelligence".

• AI applications at aitopics.org
• How AI can be applied in many fields