Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI,
the form of AI where programs are developed to perform specific tasks,
that is being utilized for a wide range of activities including medical diagnosis, electronic trading platforms, robot control, and remote sensing.
AI has been used to develop and advance numerous fields and industries,
including finance, healthcare, education, transportation, and more.
AI for Good
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.
Agriculture
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. 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.
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.
Aviation
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.
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. 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,
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.
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.
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. 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”. The autopilot records the actions of the human pilot generating learning models using artificial neural networks. The autopilot is then given full control and observed by the pilot as it executes the training exercise.
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. IAS implementation employs three phases; pilot data collection, training, and autonomous control. Baomar and Bentley's goal is to create a more autonomous autopilot to assist pilots in responding to emergency situations.
Computer science
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. 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.
Deepfakes
In June 2016, a research team from the visual computing group of the Technical University of Munich and from Stanford University developed Face2Face,
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.
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.
Education
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.
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.
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.
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. Also, AI has been known to lead to the loss of both human agency and simultaneity.
Finance
Algorithmic trading
Algorithmic trading
involves the use of complex AI systems to make trading decisions at
speeds several orders of magnitudes greater than any human is capable
of, often making millions of trades in a day without any human
intervention. Such trading is called High-frequency Trading,
and it represents one of the fastest growing sectors in financial
trading. Many banks, funds, and proprietary trading firms now have
entire portfolios which are managed purely by AI systems. Automated trading systems
are typically used by large institutional investors, but recent years
have also seen an influx of smaller, proprietary firms trading with
their own AI systems.
Market analysis and data mining
Several large financial institutions have invested in AI engines to assist with their investment practices. BlackRock’s AI engine, Aladdin,
is used both within the company and to clients to help with investment
decisions. Its wide range of functionalities includes the use of natural
language processing to read text such as news, broker reports, and
social media feeds. It then gauges the sentiment on the companies
mentioned and assigns a score. Banks such as UBS and Deutsche Bank use
an AI engine called Sqreem (Sequential Quantum Reduction and Extraction
Model) which can mine data to develop consumer profiles and match them
with the wealth management products they’d most likely want. Goldman Sachs uses Kensho,
a market analytics platform that combines statistical computing with
big data and natural language processing. Its machine learning systems
mine through hoards of data on the web and assess correlations between
world events and their impact on asset prices. Information Extraction, part of artificial intelligence, is used to extract information from live news feed and to assist with investment decisions.
Personal finance
Several
products are emerging that utilize AI to assist people with their
personal finances. For example, Digit is an app powered by artificial
intelligence that automatically helps consumers optimize their spending
and savings based on their own personal habits and goals. The app can
analyze factors such as monthly income, current balance, and spending
habits, then make its own decisions and transfer money to the savings
account.
Wallet.AI, an upcoming startup in San Francisco, builds agents that
analyze data that a consumer would leave behind, from Smartphone
check-ins to tweets, to inform the consumer about their spending
behavior.
Portfolio management
Robo-advisors
are becoming more widely used in the investment management industry.
Robo-advisors provide financial advice and portfolio management with
minimal human intervention. This class of financial advisers work based
on algorithms built to automatically develop a financial portfolio
according to the investment goals and risk tolerance of the clients. It
can adjust to real-time changes in the market and accordingly calibrate
the portfolio.
Underwriting
An
online lender, Upstart, analyzes vast amounts of consumer data and
utilizes machine learning algorithms to develop credit risk models that
predict a consumer's likelihood of default. Their technology will be
licensed to banks for them to leverage for their underwriting processes
as well.
ZestFinance developed its Zest Automated Machine Learning (ZAML)
Platform specifically for credit underwriting as well. This platform
utilizes machine learning to analyze tens of thousands of traditional
and nontraditional variables (from purchase transactions to how a
customer fills out a form) used in the credit industry to score
borrowers. The platform is particularly useful to assign credit scores
to those with limited credit histories, such as millennials.
History
The
1980s is really when AI started to become prominent in the finance
world. This is when expert systems became more of a commercial product
in the financial field. “For example, Dupont had built 100 expert
systems which helped them save close to $10 million a year.”
One of the first systems was the Protrader expert system designed by
K.C. Chen and Ting-peng Lian that was able to predict the 87-point drop
in DOW Jones Industrial Average in 1986. “The major junctions of the
system were to monitor premiums in the market, determine the optimum
investment strategy, execute transactions when appropriate and modify
the knowledge base through a learning mechanism.”
One of the first expert systems that helped with financial plans was
created by Applied Expert Systems (APEX) called the PlanPower. It was
first commercially shipped in 1986. Its function was to help give
financial plans for people with incomes over $75,000 a year. That then
led to the Client Profiling System that was used for incomes between
$25,000 and $200,000 a year.
The 1990s was a lot more about fraud detection. One of the systems that
was started in 1993 was the FinCEN Artificial Intelligence system
(FAIS). It was able to review over 200,000 transactions per week and
over two years it helped identify 400 potential cases of money
laundering which would have been equal to $1 billion.
Although expert systems did not last in the finance world, it did help
jump-start the use of AI and help make it what it is today.
Heavy industry
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.
Hospitals and medicine
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
- Companion robots for the care of the elderly
- Mining medical records to provide more useful information.
- Design treatment plans.
- Assist in repetitive jobs including medication management.
- Provide consultations.
- Drug creation
- Using avatars in place of patients for clinical training
- 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.
IDx's first solution, IDx-DR, is the first autonomous AI-based diagnostic system authorized for commercialization by the FDA.
Human resources and recruiting
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.
Job search
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.[citation needed]
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.
Marketing
Media and e-commerce
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.
Military
The United States and other nations are developing AI applications for a range of military functions.
The main military applications of Artificial Intelligence and Machine
Learning are to enhance C2, Communications, Sensors, Integration and
Interoperability.
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.
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). AI has been incorporated into military operations in Iraq and Syria.
Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015. Military drones capable of autonomous action are widely considered a useful asset. Many artificial intelligence researchers seek to distance themselves from military applications of AI.
Music
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. The algorithm behind Emily Howell is registered as a US patent.
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. It achieved a world first by becoming the first virtual composer to be recognized by a musical professional association.
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.
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.
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 musicians, such as Taryn Southern have been collaborating with the project to create music.
News, publishing and writing
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. Similarly, the company Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football. The company is projected to generate one billion stories in 2014, up from 350 million in 2013. The organisation OpenAI has also created an AI capable of writing text.
Echobox is a software company that helps publishers increase
traffic by 'intelligently' posting articles on social media platforms
such as Facebook and Twitter.
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.
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.
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.
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.
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."
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.
This particular field continues to gain interest. In 2016, a Japanese
AI co-wrote a short story and almost won a literary prize.
Online and telephone customer service
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. It can avail for enterprises to reduce their operation and training cost. A major underlying technology to such systems is natural language processing. Pypestream uses automated customer service for its mobile application designed to streamline communication with customers.
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.
Power electronics
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.
Sensors
Artificial Intelligence has been combined with many sensor technologies, such as Digital Spectrometry by IdeaCuria Inc. which enables many applications such as at home water quality monitoring.
Telecommunications maintenance
Many telecommunications companies make use of
heuristic search in the management of their workforces, for example BT Group has deployed heuristic search in a scheduling application that provides the work schedules of 20,000 engineers.
Toys and games
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.
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.
Transportation
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. 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.
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
tool chains have been proposed.
Wikipedia
Studies related to Wikipedia
have been using artificial intelligence to support various operations.
Two of the most important areas are automatic detection of vandalism and data quality assessment in Wikipedia.
The team at the Wikimedia Foundation released a model that is designed to detect vandalism, spam, and personal attack. This model can also help students write better Wikipedia articles.
