Local African ceremony in Benin featuring a zangbeto.
The traditional African religions (or traditional beliefs and practices of African people) are a set of highly diverse beliefs that includes various ethnic religions. Generally, these traditions are oral rather than scriptural, include belief in an amount of higher and lower gods, sometimes including a supreme creator, belief in spirits, veneration of the dead, use of magic and traditional African medicine. Most religions can be described as animistic with various polytheistic and pantheistic aspects. The role of humanity is generally seen as one of harmonizing nature with the supernatural.
According to the author Lugira, "it is the only religion that can claim
to have originated in Africa. Other religions found in Africa have
their origins in other parts of the world."
Spread
An early-20th-century Igbo medicine man in Nigeria, West Africa
Adherents of traditional religions in Sub-Saharan Africa are distributed among 43 countries and are estimated to number over 100 million.
Although the majority of Africans today are adherents of Christianity or Islam, African people often combine the practice of their traditional belief with the practice of Abrahamic religions.
The two Abrahamic religions are widespread across Africa, though mostly
concentrated in different areas. They have replaced indigenous African
religions, but are often adapted to African cultural contexts and belief
systems.
Followers of traditional African religions are also found around the world. In recent times, traditional religions, such as the Yoruba religion, are on the rise. The religion of the Yoruba is finding roots in the United States among African Americans and some others.
Traditional African religions can be broken down into linguistic cultural groups, with common themes. Among Niger–Congo-speakers
is a belief in a creator God or higher deity, which is considered by
some to be a widespread and ancient feature of Niger-Congo-cultures,
along with other more specialized deities, ancestor spirits,
territorial spirits, evil caused by human ill will and neglecting
ancestor spirits, and priests of territorial spirits. New world religions such as Santería, Vodun, and Candomblé, would be derived from this world. Among Nilo-Saharan
speakers is the belief in Divinity; evil is caused by divine judgement
and retribution; prophets as middlemen between Divinity and man. Among Afro-Asiatic-speakers is henotheism,
the belief in one's own gods but accepting the existence of other gods;
evil here is caused by malevolent spirits. The Semitic Abrahamic religion of Judaism is comparable to the latter world view. San religion is non-theistic but a belief in a Spirit or Power of existence which can be tapped in a trance-dance; trance-healers.
Some researchers, including historical ethnolinguist Christopher Ehret,
suggest that monotheistic concepts, including the belief in a creator
god or force (along with the veneration of many lesser deities and
spirits) are ancient and indigenous among peoples of the Niger-Congo
ethnolinguistic family (of much of West Africa and Central Africa) and
date to the beginning of their history, in a form substantially
different from the monotheism found in Abrahamic religions. Traditional
Niger-Congo religion also included polytheistic and animistic elements.
Traditional African medicine
is also directly linked to traditional African religions. According to
Clemmont E. Vontress, the various religious traditions of Africa are
united by a basic Animism. According to him, the belief in spirits and
ancestors is the most important element of African religions. Gods were
either self-created or evolved from spirits or ancestors which got
worshiped by the people. He also notes that most modern African folk
religions were strongly influenced by non-African religions, mostly
Christianity and Islam and thus may differ from the ancient forms.
Ceremonies
West
and Central African religious practices generally manifest themselves
in communal ceremonies or divinatory rites in which members of the
community, overcome by force (or ashe, nyama,
etc.), are excited to the point of going into meditative trance in
response to rhythmic or driving drumming or singing. One religious
ceremony practiced in Gabon and Cameroon is the Okuyi, practiced by several Bantu
ethnic groups. In this state, depending upon the region, drumming or
instrumental rhythms played by respected musicians (each of which is
unique to a given deity or ancestor), participants embody a deity or
ancestor, energy or state of mind by performing distinct ritual
movements or dances which further enhance their elevated consciousness.
When this trance-like state is witnessed and understood,
adherents are privy to a way of contemplating the pure or symbolic
embodiment of a particular mindset or frame of reference. This builds
skills at separating the feelings elicited by this mindset from their
situational manifestations in daily life. Such separation and subsequent
contemplation of the nature and sources of pure energy or feelings
serves to help participants manage and accept them when they arise in
mundane contexts. This facilitates better control and transformation of
these energies into positive, culturally appropriate behavior, thought,
and speech. Also, this practice can also give rise to those in these
trances uttering words which, when interpreted by a culturally educated
initiate or diviner, can provide insight into appropriate directions
which the community (or individual) might take in accomplishing its
goal.
Spirits
Followers of traditional African religions pray to various spirits as well as to their ancestors.
This includes also nature, elementary and animal spirits. The
difference between powerful spirits and gods is often minimal. Most
African societies believe in several “high gods” and a large amount of
lower gods and spirits. There are also religions with a single Supreme
being (Chukwu, Nyame, Olodumare, Ngai, Roog, etc.). Some recognize a dual god and goddess such as Mawu-Lisa.
Traditional African religions generally believe in an afterlife, one or more Spirit worlds, and Ancestor worship
is an important basic concept in mostly all African religions. Some
African religions adopted different views through the influence of Islam
or even Hinduism.
There are more similarities than differences in all traditional African religions. The deities and spirits are honored through libation or sacrifice (of animals,
vegetables, cooked food, flowers, semi-precious stones and precious
metals). The will of the gods or spirits is sought by the believer also
through consultation of divinities or divination.
Traditional African religions embrace natural phenomena – ebb and tide,
waxing and waning moon, rain and drought – and the rhythmic pattern of
agriculture. According to Gottlieb and Mbiti:
The
environment and nature are infused in every aspect of traditional
African religions and culture. This is largely because cosmology and
beliefs are intricately intertwined with the natural phenomena and
environment. All aspects of weather, thunder, lightning, rain, day,
moon, sun, stars, and so on may become amenable to control through the
cosmology of African people. Natural phenomena are responsible for
providing people with their daily needs.
For example, in the Serer religion, one of the most sacred stars in the cosmos is called Yoonir (the Star of Sirius). With a long farming tradition, the Serer high priests and priestesses (Saltigue) deliver yearly sermons at the Xooy Ceremony (divination ceremony) in Fatick before Yoonir's phase in order to predict winter months and enable farmers to start planting.
Traditional healers are common in most areas, and their practices include a religious element to varying degrees.
Since Africa is a large continent with many ethnic groups and
cultures, there is not one single technique of casting divination. The
practice of casting may be done with small objects, such as bones,
cowrie shells, stones, strips of leather, or flat pieces of wood.
Some castings are done using sacred divination plates made of wood or performed on the ground (often within a circle).
In traditional African societies, many people seek out diviners
on a regular basis. There are generally no prohibitions against the
practice. Diviner (also known as priest) are also sought for their
wisdom as counselors in life and for their knowledge of herbal medicine.
Virtue and vice
Virtue
in traditional African religion is often connected with carrying out
obligations of the communal aspect of life. Examples include social
behaviors such as the respect for parents and elders, raising children
appropriately, providing hospitality, and being honest, trustworthy, and
courageous.
In some traditional African religions, morality is associated
with obedience or disobedience to God regarding the way a person or a
community lives. For the Kikuyu, according to their primary supreme creator, Ngai, acting through the lesser deities, is believed to speak to and be capable of guiding the virtuous person as one's conscience.
In many cases, Africans who have converted to other religions
have still kept up their traditional customs and practices, combining
them in a syncretic way.
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.
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.
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
X-ray of a hand, with automatic calculation of bone age by a computer software
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 SportsFantasy 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.
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.
Artificial intelligence (AI) has a range of uses in government. It can be used to further public policy
objectives (in areas such as emergency services, health and welfare),
as well as assist the public to interact with the government (through
the use of virtual assistants, for example). According to the Harvard Business Review,
"Applications of artificial intelligence to the public sector are broad
and growing, with early experiments taking place around the world." Hila Mehr from the Ash Center for Democratic Governance and Innovation at Harvard University notes that AI in government is not new, with postal services using machine methods in the late 1990s to recognise handwriting on envelopes to automatically route letters.
The use of AI in government comes with significant benefits, including
efficiencies resulting in cost savings, for instance by reducing the
number of front office staff, and reducing the opportunities for
corruption, but it also carries risks.
Uses of AI in government
The potential uses of AI in government are wide and varied, with Deloitte considering that "Cognitive technologies could eventually revolutionize every facet of government operations". Mehr suggests that six types of government problems are appropriate for AI applications:
Resource allocation - such as where administrative support is required to complete tasks more quickly.
Large datasets - where these are too large for employees to work
efficiently and multiple datasets could be combined to provide greater
insights.
Experts shortage - including where basic questions could be answered and niche issues can be learned.
Predictable scenario - historical data makes the situation predictable.
Procedural - repetitive tasks where inputs or outputs have a binary answer.
Diverse data - where data takes a variety of forms (such as visual and linguistic) and needs to be summarised regularly.
Meher states that "While applications of AI in government work have
not kept pace with the rapid expansion of AI in the private sector, the
potential use cases in the public sector mirror common applications in
the private sector."
Potential and actual uses of AI in government can be divided into
three broad categories: those that contribute to public policy
objectives; those that assist public interactions with the government;
and other uses.
Contributing to public policy objectives
There are a range of examples of where AI can contribute to public policy objectives. These include:
Receiving benefits at job loss, retirement, bereavement and
child birth almost immediately, in an automated way (thus without
requiring any actions from citizens at all)
Social insurance service provision
Classifying emergency calls based on their urgency (like the system used by the Cincinnati Fire Department in the United States)
Detecting and preventing the spread of diseases
Assisting public servants in making welfare payments and immigration decisions
Adjudicating bail hearings
Triaging health care cases
Monitoring social media for public feedback on policies
Monitoring social media to identify emergency situations
Identifying fraudulent benefits claims
Predicting a crime and recommending optimal police presence
Predicting traffic congestion and car accidents
Anticipating road maintenance requirements
Identifying breaches of health regulations
Providing personalised education to students
Marking exam papers
Assisting with defence and national security.
Making symptom based health ChatbotAI Vaid for diagnosis
Assisting public interactions with government
AI can be used to assist members of the public to interact with government and access government services, for example by:
Directing requests to the appropriate area within government
Filling out forms
Assisting with searching documents (e.g. IP Australia’s trade mark search)
Scheduling appointments
Examples of virtual assistants or chatbots being used by government include the following:
Launched in February 2016, the Australian Taxation Office has a virtual assistant on its website called "Alex".
As at 30 June 2017, Alex could respond to more than 500 questions, had
engaged in 1.5 million conversations and resolved over 81% of enquiries
at first contact.
Australia's National Disability Insurance Scheme (NDIS) is developing a virtual assistant called "Nadia" which takes the form of an avatar using the voice of actor Cate Blanchett. Nadia is intended to assist users of the NDIS to navigate the service. Costing some $4.5 million, the project has been postponed following a number of issues. Nadia was developed using IBM Watson, however, the Australian Government is considering other platforms such as Microsoft Cortana for its further development.
The Australian Government's Department of Human Services uses virtual assistants on parts of its website to answer questions and encourage users to stay in the digital channel.
As at December 2018, a virtual assistant called "Sam" could answer
general questions about family, job seeker and student payments and
related information. The Department also introduced an internally-facing
virtual assistant called "MelissHR" to make it easier for departmental
staff to access human resources information.
AI offers potential efficiencies and costs savings for the government. For example, Deloitte has estimated that automation could save US Government
employees between 96.7 million to 1.2 billion hours a year, resulting
in potential savings of between $3.3 billion to $41.1 billion a year. The Harvard Business Review
has stated that while this may lead a government to reduce employee
numbers, "Governments could instead choose to invest in the quality of
its services. They can re-employ workers’ time towards more rewarding
work that requires lateral thinking, empathy, and creativity — all
things at which humans continue to outperform even the most
sophisticated AI program."
Potential risks
Potential risks associated with the use of AI in government include AI becoming susceptible to bias, a lack of transparency in how an AI application may make decisions, and the accountability for any such decisions.
Government by algorithm (also known as Algorithmic regulation, Regulation by algorithms, Algorithmic governance, Algorithmic legal order or Algocracy) is an alternative form of government or social ordering, where the usage of computer algorithms, especially of artificial intelligence and blockchain,
is applied to regulations, law enforcement, and generally any aspect of
everyday life such as transportation or land registration. Alternatively, algorithmic regulation is defined as setting the
standard, monitoring and modification of behaviour by means of
computational algorithms — automation of judiciary is in its scope.
Government by algorithm raises new challenges that are not captured in the e-Government literature and the practice of public administration. Some sources equate cyberocracy, which is a hypothetical form of government that rules by the effective use of information, with algorithmic governance, although algorithms are not the only means of processing information. Nello Cristianini and Teresa Scantamburlo argued that the combination of a human society and an algorithmic regulation forms a social machine.
History
In 1962, head of the Department of technical physics in Kiev, Alexander Kharkevich, published an article in the journal "Communist" about a computer network for processing of information and control of economy. In fact, he proposed to make a network like the modern Internet for the needs of algorithmic governance.
Also in the 1960s and 1970s, Herbert A. Simon championed expert systems as tools for rationalization and evaluation of administrative behavior. The automation of rule-based processes was an ambition of tax agencies over many decades resulting in varying success. Early work from this period includes Thorne McCarty's influential TAXMAN project in the US and Ronald Stamper's LEGOL project in the UK. The Honourable Justice Michael Kirby published a paper in 1998, where he expressed optimism that the then-available computer technologies such as legal expert system could evolve to computer systems, which will strongly affect the practice of courts. In 2006, attorney Lawrence Lessig known for the slogan "Code is law" wrote:
"[T]he invisible hand of cyberspace is building an architecture that is quite the opposite of its
architecture at its birth. This invisible hand, pushed by government and by commerce, is constructing
an architecture that will perfect control and make highly efficient regulation possible"
Written
laws are not replaced but stressed to test its efficiency. Algorithmic
regulation is supposed to be a system of governance where more exact
data collected from citizens via their smart devices and computers are
used for more efficiency in organizing human life as a collective. As Deloitte
estimated in 2017, automation of US government work could save 96.7
million federal hours annually, with a potential savings of $3.3
billion; at the high end, this rises to 1.2 billion hours and potential
annual savings of $41.1 billion. According to a study of Stanford University, 45% of the studied US federal agencies have experimented with AI and related machine learning (ML) tools up to 2020.
In 2013, algorithmic regulation was coined by Tim O'Reilly, Founder and CEO of O'Reilly Media Inc.:
Sometimes the "rules" aren't really even rules. Gordon
Bruce, the former CIO of the city of Honolulu, explained to me that when
he entered government from the private sector and tried to make
changes, he was told, "That's against the law." His reply was "OK. Show
me the law." "Well, it isn't really a law. It's a regulation." "OK. Show
me the regulation." "Well, it isn't really a regulation. It's a policy
that was put in place by Mr. Somebody twenty years ago." "Great. We can
change that!""
[...]
Laws should specify goals, rights, outcomes, authorities, and limits. If
specified broadly, those laws can stand the test of time.
Regulations, which specify how to execute those laws in much more
detail, should be regarded in much the same way that programmers regard
their code and algorithms, that is, as a constantly updated toolset to
achieve the outcomes specified in the laws.
[...]
It's time for government to enter the age of big data. Algorithmic
regulation is an idea whose time has come.
A 2019 poll made by Center for the Governance of Change at IE University
in Spain showed that 25% of citizens from selected European countries
are somewhat or totally in favor of letting an artificial intelligence
make important decisions about the running of their country. The following table shows detailed results:
53% of these applications were produced by in-house experts. Commercial providers of residual applications include Palantir Technologies. From 2012, NOPD started a secretive collaboration with Palantir Technologies in the field of predictive policing. According to the words of James Carville, he was impetus of this project and "[n]o one in New Orleans even knows about this".
AI politicians
In 2018, an activist named Michihito Matsuda ran for mayor in the Tama city area of Tokyo as a human proxy for an artificial intelligence program. While election posters and campaign material used the term 'robot', and displayed stock images of a feminine android, the 'AI mayor' was in fact a machine learning algorithm trained using Tama city datasets. The project was backed by high-profile executives Tetsuzo Matsumoto of Softbank and Norio Murakami of Google. Michihito Matsuda came third in the election, being defeated by Hiroyuki Abe. Organisers claimed that the 'AI mayor' was programmed to analyze citizen petitions put forward to the city council in a more 'fair and balanced' way than human politicians.
In 2019, AI-powered messenger chatbot SAM participated in the discussions on social media connected to electoral race in New Zealand.
The creator of SAM, Nick Gerritsen, believes SAM will be advanced
enough to run as a candidate by late 2020, when New Zealand has its next
general election.
AI judges
According to the statement of Beijing Internet Court, China is the first country to create an internet court or cyber court. Chinese AI judge is a virtual recreation
of an actual female judge. She "will help the court's judges complete
repetitive basic work, including litigation reception, thus enabling
professional practitioners to focus better on their trial work".
Also Estonia plans to employ artificial intelligence to decide small-claim cases of less than €7,000.
COMPAS software is used in USA to assess the risk of recidivism in courts.
Reputation systems
Tim O'Reilly suggested that data sources and reputation systems combined in algorithmic regulation can outperform traditional regulations.
For instance, once taxi-drivers are rated by passengers, the quality of
their services will improve automatically and "drivers who provide poor
service are eliminated". O'Reilly's suggestion is based on control-theoreric concept of feed-back loop — improvements and disimprovements of reputation enforce desired behavior. The usage of feed-loops for the management of social systems is already been suggested in management cybernetics by Stafford Beer before.
The Chinese Social Credit System is closely related to China's mass surveillance systems such as the Skynet, which incorporates facial recognition system, big data analysistechnology and AI. This system provides assessments of trustworthiness of individuals and businesses. Among behavior, which is considered as misconduct by the system, jaywalking and failing to correctly sort personal waste are cited. Behavior listed as positive factors of credit ratings includes donating blood, donating to charity, volunteering for community services, and so on.
Chinese Social Credit System enables punishments of "untrustworthy"
citizens like denying purchase of tickets and rewards for "trustworthy"
citizen like less waiting time in hospitals and government agencies.
Management of infection
In February 2020, China launched a mobile app to deal with Coronavirus outbreak.
Users are asked to enter their name and ID number. The app is able to
detect 'close contact' using surveillance data and therefore a potential
risk of infection. Every user can also check the status of three other
users. If a potential risk is detected, the app not only recommends
self-quarantine, it also alerts local health officials.
Cellphone data is used to locate infected patients in South Korea, Taiwan, Singapore and other countries.
In March 2020, the Israeli government enabled security agencies to
track mobile phone data of people supposed to have coronavirus. The
measure was taken to enforce quarantine and protect those who may come
into contact with infected citizens. Also in March 2020, Deutsche Telekom shared private cellphone data with the federal government agency, Robert Koch Institute, in order to research and prevent the spread of the virus. Russia deployed facial recognition technology to detect quarantine breakers. Italian regional health commissioner Giulio Gallera said that "40% of people are continuing to move around anyway", as he has been informed by mobile phone operators. In USA, Europe and UK, Palantir Technologies is taken in charge to provide COVID-19 tracking services.
Blockchain
Cryptocurrencies, Smart Contracts and Decentralized Autonomous Organization are mentioned as means to replace traditional ways of governance. Cryptocurrencies are currencies, which are enabled by algorithms without a governmental central bank. Smart contracts are self-executable contracts, whose objectives are the reduction of need in trusted governmental intermediators, arbitrations and enforcement costs. A decentralized autonomous organization is an organization represented by smart contracts that is transparent, controlled by shareholders and not influenced by a central government.
Criticism
The are potential risks associated with the use of algorithms in government. Those include algorithms becoming susceptible to bias, a lack of transparency in how an algorithm may make decisions, and the accountability for any such decisions. There is also a serious concern that gaming by the regulated parties might occur, once more transparency is brought into the decision making by algorithmic governance, regulated parties might try to manipulate their outcome in own favor and even use adversarial machine learning. According to Harari,
the conflict between democracy and dictatorship is seen as a conflict
of two different data-processing systems — AI and algorithms may swing
the advantage toward the latter by processing enormous amounts of
information centrally. Also, the contributors in the 2019's documentary iHuman express apprehension of "infinitely stable dictatorships" being created by governmental use of AI.
Regulation of algorithmic governance
The Netherlands employed an algorithmic system SyRI (Systeem Risico
Indicatie) to detect citizens perceived being high risk for committing welfare fraud, which quietly flagged thousands of people to investigators. This caused a public protest. The district court of Hague shut down SyRI referencing Article 8 of the European Convention on Human Rights (ECHR).
