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{{Paper
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|title=Artificial Intelligence Technologies: Applications, Threats, and Future Opportunities
|pdfUrl=https://ceur-ws.org/Vol-3283/Paper107.pdf
|volume=Vol-3283
|authors=Ayodeji Olalekan Salau,Wubetu Barud Demilie,Adedeji Tomide Akindadelo,Joy Nnenna Eneh
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==Artificial Intelligence Technologies: Applications, Threats, and Future Opportunities==
https://ceur-ws.org/Vol-3283/Paper107.pdf
Artificial Intelligence Technologies: Applications, Threats, and
Future Opportunities
Ayodeji Olalekan Salau1, Wubetu Barud Demilie2, Adedeji Tomide Akindadelo3, Joy Nnenna
Eneh4
1
Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria
2
Department of Information Technology, Wachemo University, Hossana, Ethiopia
3
Department of Basic Sciences, Babcock University, Ilishan Remo, Nigeria
4
Department of Electrical Engineering, University of Nigeria, Nsukka, Nigeria
Abstract
Artificial intelligence's (AI) rapid evolution has made it a critical technology in medicine,
education, research, computer vision, natural language processing, automatic driving, robotics
and automation, and other fields such as retail (marketing) services across all industries. It has
experienced notable advancements in these areas over the past decade giving rise to numerous
future opportunities, and associated challenges. Furthermore, artificial intelligence systems
have an impact on work efficiency and activities in industries. While artificial intelligence is
being positively embraced by sectors such as healthcare, education, industries, and marketing
service providers, its application provides both opportunities and challenges. Rapid advances
in artificial intelligence and related technologies will assist service providers in creating new
value for their customers, while also improving the effectiveness of their operational processes.
Efficient deployment of artificial intelligence requires planning and tactics to transform the
needed technologies. This study investigates the state of artificial intelligence-based
technological applications and their impacts on various sectors such as healthcare, education,
and marketing (retail) industries. The results indicate that the healthcare, education, and
marketing industries at present are using artificial intelligence-enabled systems to perform
different activities for a wide range of services. Furthermore, the authors present the future
opportunities derived from AI technologies and associated threats in the various sectors
reviewed.
Keywords 1
Artificial Intelligence, Deep Learning, Future Opportunities, Machine Learning, Threat.
1. Introduction
Artificial intelligence (AI) has advanced at an exponential rate in recent years. Artificially intelligent
technologies can sift through and interpret massive amounts of data from various sources in order to
perform a variety of tasks [1], [2]. To improve quality of life and operational efficiency, healthcare,
education, and marketing service providers around the world, particularly in developed economies, are
aggressively deploying digital technologies such as artificial intelligence (AI), machine learning (ML),
big data analytics, smart sensors and robots, and the Internet of Things (IoT) [3-5]. AI-based
technologies, such as ML, natural language processing, and smart robots, offer tremendous prospects
for innovation in knowledge-intensive industries [6]. The term "Artificial Intelligence (AI)" refers to
machine intelligence. It has grown increasingly popular in recent years, and is now used to learn and
emulate human behavior. These machines are capable of learning from their mistakes and perform
functions similar to those performed by humans. As AI advances, it will have a huge impact on our
quality of life [7-10] and AI-based models which have been trained using large amounts of data are
capable of making intelligent decisions [11-14]. Machine learning is an aspect of AI that focuses on the
ACI’22: Workshop on Advances in Computation Intelligence, its Concepts & Applications at ISIC 2022, May 17-19, Savannah, United States
EMAIL: ayodejisalau98@gmail.com (A. 1)
ORCID: 0000-0002-6264-9783 (A. 1), 0000-0001-8034-154X (A. 2)
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
265
implementation of AI technologies in various applications. The goal of ML is to continuously learn
from new data to make predictions or perform actions without the need for explicit conversion.
Researchers began exploring the potential of creating a consultation machine in the 1940s, and AI
research began in earnest in the 1950s. The study in [16] has been hailed as a fundamental philosophical
argument for AI decision-making. The creation of computer algorithms that can compete with human
reasoning in essential games like checkers and chess, for example, is a top priority. AI research has
gotten a lot of attention since then, particularly following three triumphs. Deep Blue, IBM's first
triumph, was the largest chess car capable of defeating the world chess champion [17]. The work in
[18] was the second achievement, while the third achievement was an AI chess player that achieved a
high level of play after only 24 hours of play and defeated the best human chess player in 2017 [19].
These descriptive moments have served as motivation for the tremendous breakthroughs in AI and its
applications in ML over the last two decades, attracting considerable resources to AI research and
development. Some organizations, for instance spent $26 to $39 billion on AI in 2016, which was the
same as in 2013 [20]. Supply chains have frequently aided in the gain of new investments and interests
in operational implant technologies. Product distribution, web hosting, remote development, and
intelligent sensors are examples of high-level data frameworks that have become significant drivers of
corporate development. Business cycles and supply networks, even in different enterprises and
topographies are currently thought to be constantly linked [21]. In the business sector, AI applications
have improved job profitability by allowing for easy understanding and anticipation of consumer
interests, thanks to cutting-edge technology and robotization.
The expected value of AI, according to McKinsey's new report, is $3.5 to $5.8 trillion, with $0.2 to $0.3
billion allocated to medical care management, $0.7 to $1.0 billion allocated to integration (automotive
collection, advanced gadgets, and semiconductors, as well as advanced integrated design), and $0.4 to
$0.8 billion allocated to sales [22], [23]. ML applications have shown capability to fully reduce
healthcare costs (up to $300 billion annually), improve online deals (up to 30%), improve stock (up to
50%), improve delivery (up to 30%), and improve natural product quality (up to 10%) [20], [23].
While the automobile industry and new organizations began to lead the pack in AI adoption, retail and
healthcare establishments, where AI could deliver more profits, established pricing that was generally
limited and low [21]. Some industries, such as automotive, telecommunications, and healthcare, have
reaped greater benefits from AI implementation than others [24]. Similarly, different industries use
different AI applications. Clear barriers of adoption have been identified in less digitally mature sectors,
including both internal (e.g., cultural resistance, lack of skills, financial considerations) and external
barriers (e.g., lack of venture capital). The challenges of AI adoption are mostly comparable to those
that hamper digitalization, especially for medium-sized businesses. Adoption of such AI applications
are predicted to have a variety of positive consequences on individual businesses, value chains, and
social and macroeconomic levels. Therefore, in this study, we examine the current state of artificial
intelligence-based technological applications and their impact is on healthcare, education, and
marketing (retail) industries.
This paper is structured as follows. Section 2 presents the application of AI technologies. Section 3
presents the threats and opportunities of AI. Section 4 presents the rise of AI, overall future outlooks,
and new risks. The conclusion of the paper is presented in section 5.
2. Applications of Artificial Intelligence Technologies
2.1. AI in Healthcare Services
The most effective use of medical artificial intelligence is to provide solutions that medical experts
cannot achieve [25]. AI technologies are rapidly taking over the healthcare sector. AI has aided the
transformation of the manual healthcare system into an automated one. The technical difficulties of
digitizing healthcare presents new issues when developers create AI systems to perform tasks [26], [27].
Furthermore, increase in population is expected to increase the demand for healthcare services. The
healthcare sector requires creative solutions to become more effective and efficient without incurring
excessive costs [27].
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The healthcare sector can benefit from quick technological breakthroughs, notably in the fields of AI
and robotics. AI and robotics in healthcare are quickly developing, especially for applications in early
detection and diagnostics. AI has become more powerful by successfully completing in human tasks
more quickly, easily, and economically. The use of AI also comes with it various hazards and
difficulties, such as the risk of patient harm from system flaws, patient privacy issues when acquiring
data and others. AI is essential for early detection and diagnosis, and it is applied in numerous ways to
reliably, consistently, and swiftly detect diseases like cancer. Simply, it autonomously learns and detects
associations and recommends a diagnosis. Because AI has demerits, the future of AI in healthcare is
not entirely optimistic. Numerous questions have arisen regarding whether AI can exercise doctors'
rights and obligations, as well as protect privacy concerns, and the applicable laws are not fully prepared
for this advancement. Although it appears that present laws encourage AI given its application in the
global healthcare system. It has been established that guidelines for the creation, modification, and use
of technology and health technology products to medical care are available. Online processes to acquire
therapeutic knowledge have been established for many applications, such as a procedure or schedule of
a medical procedure, symptomatic screening, and the diagnosis and prediction of certain disorders [28].
For example, the IBM AI analyzes mammographic images and clinical records to predict a specific
disease a year ahead of time with 87% accuracy, incorporating dysfunction, thyroid strength, biopsies,
laboratory tests, and codes from multiple findings was presented in [29]. Also, for sugar IQ, a diabetic
management software that employs an algorithm was developed to adjust blood glucose levels, insulin
profiles, hypoglycemia scores, and dietary supplements to give clients the glucose experience they
expect and anticipate was presented in [30].
2.2. AI in Educational Services
The emergence of innovative technologies in the educational sector has an impact on the methods of
teaching and learning [31, 32]. With the rapid development of AI technology in recent years, its use in
education has become important, especially during the COVID-19 pandemic [33-35].
2.2.1. Student needs can be adapted to educational software
One of the most important installations that can aid effective participation and learning in primary,
secondary, and tertiary institutions is the use of advanced software to acquire tailored knowledge in one
of the preceding styles with the use of apps, games, and software [36].
2.2.2. AI can identify areas in which courses need to be improved
Teachers most times may not be aware of gaps in communication while teaching their courses and in
teaching materials given to students. AI offers can be made to offer solutions to these difficulties [36].
2.2.3. AI can help students with additional tutors
Educational tools, particularly those geared toward artificial intelligence, are now available to help
college students with math, writing, and other subjects [36].
2.2.4. Students and instructors can benefit from AI-driven programs
AI can give feedback on the overall effectiveness of the classes in addition to assisting teachers and
students in developing lessons that are tailored to each individual's needs. Some universities, especially
those that offer internet services, use AI systems to monitor student progress and notify
instructors/teachers when a problem arises with their students' performance [36].
2.2.5. AI is changing the way we find and use information
Non-intelligence agencies that have an impact on the records we view and receive daily are most times
not discussed in literature. For instance, Google adjusts to customer requirements and orders, Amazon
prepares recommendations based on prior purchases, Siri adapts to customer needs and orders, and
nearly all online adverts are personalized to customer hobbies and purchasing interests [36].
2.2.6. The role of teachers could be changed by AI
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Educators have a part in training students, although this job and what it entails can be altered by a new
generation standing on the shoulders of smart computing programs. AI, as we have pointed out, can
take on tasks such as grading, it can assist college students in developing abilities, and can be used as
another real worldwide teaching tool. The AI programs can be created to deliver information, serve as
a forum for college students to ask questions and learn facts, or provide a basic guideline for the teacher.
AI, on the other hand, may in extreme situations shift the instructor's role to that of a facilitator.
2.2.7. AI can make trial-and-error learning easier
Despite the fact that trial and error is an important part of learning, many students are afraid of failing
or losing their grasp on a solution. As humans, most people don't want to learn from mistakes.
2.2.8. AI-based data can change the way schools find teachers and
support students
Intelligent data collection aided by clever computer systems have altered the way schools connect with
prospective students in the past and present. AI has been useful from hiring teachers to assisting college
students in selecting suitable guides to help achieve the students' desires and dreams.
2.3. AI in retail (marketing) services
A wide range of marketing researchers have recently become intrigued by AI [37]. Their research
advances this developing field by taking a global perspective on AI marketing tools. Three levels of
analysis, country, company, and consumer were the focus of their study. The country-level analysis
highlights how different levels of economic inequality exist between nations due to the significant
financial resources needed for AI implementation. Globalization is the main emphasis of the company-
level evaluation. Because the hardware supporting these technologies is worldwide in scope, cultural
adaptation is necessary for their implementation. The consumer-level analysis looks at consumer ethics
and privacy concerns, as AI technologies collect, store, and process a plethora of personal data from all
over the world. The researchers focused on two important dimensions of AI technologies in marketing
through the prism of these three lenses: (1) human-machine interaction, and (2) automated analysis of
text, audio, images, and video. They use a three-part global perspective to explore the relationship
between these two important dimensions of AI and provide a set of research questions for future
marketing enthusiasts in this burgeoning sector. In marketing as well, rational ingenuity is employed.
Electronic shopping allows online businesses to collect massive amounts of data about purchases,
bushels, and, events, design processes. This data offers merchants information on future expectations,
advancements, and aid with store network operations, stock satisfaction, and delivery structures. For
example, Walmart utilizes a ML picture algorithm named "Eden" in-group products for their brand
which predicts when it would go bad [38]. Home Depot uses critical thinking to foresee stock market
possibilities and robotics in stock options, whereas the Kroger Company is attempting to develop a
more limited approach to distribution center testing to fulfill client requests [38].
Some traditional merchants have begun to offer basic food products for delivery to competing internet
retailers. Amazon Fresh, Instacart, Peapod, and Tesco, for example, provide essential products to their
online customers. Developing a competent e-staple distribution system necessitates addressing many
concerns, including issues of stochastic setting arrangements with user experience, a targeted vehicle
that directs issues through time windows, and stock management issues. This problem structure is not
limited to e-goods as it also applies to other end-to-end services such as home health care delivery,
logistics, and institutional management, as well as some home management services such as gas,
energy, and water [39]. Home management, while being the most expensive, polluting, and inefficient
production network activity provides consumers with a wide range of focus areas, such as a wide
selection of commodities, price comparisons between E-food feeds, and easy sales imports [40]. A
review of research on AI technologies in marketing is vital, according to [37], with an emphasis on
whether the studies take into account these two aspects of AI (HMI and automated analysis), as well as
the aspects of its global lens of inequality, globalization, ethics, and privacy.
3. The threats and opportunities of AI
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Applications of AI can increase value by personalizing goods, enhancing customer service, and helping
to create new product categories, business models, and even industries. AI applications also benefit the
workforce (such as improved workplace safety) and are expected to provide significant societal and
economic-wide benefits in addition to the firm-level benefits and prospects. Significant increments in
productivity, innovation, growth, and job creation have been predicted using AI in recent times. Labor
productivity is expected to rise by 11-37% by 2035, according to [24]. Furthermore, AI is predicted to
favorably contribute to the United Nations Sustainable Development Goals, and the capabilities of AI
and ML to handle important health concerns, such as the present COVID-19 health epidemic, are
remarkable [24]. AI systems, for example, has the potential to shorten the time it takes to develop
vaccines and drugs. However, implementing AI is fraught with difficulties. Although AI has significant
advantages for the workforce, it is clear that a large number of professions will be eliminated or not be
useful as a result of AI. Although this will free up human resources for roles requiring higher levels of
competence, workforce adaptation in the form of education and retraining is essential because those
who are displaced frequently lack the skills necessary to take advantage of AI-driven job creation.
Furthermore, when it comes to AI adoption, SMEs confront specific hurdles, whereas large
organizations are better positioned to profit on AI's opportunities. This could lead to a market
overconcentration of major enterprises. Significant ethical and legal issues also exist, including those
relating to the security of AI systems, data privacy and protection, transparency and accountability,
discrimination, justification, and responsibility. Given its potential, governments everywhere are
engaged in a fierce race in AI, driven by worries about geopolitics, the economy, and technology [37].
When it comes to the United States and China, each has a unique balance of strengths. For example,
the European Union and the United States are nearly equal and ahead of China in terms of AI expertise
and research skills, however Europe trails in terms of venture capital funding, practical use, and
hardware development. The European Union, on the other hand, has long enjoyed competitive
advantages in many critical areas, including automotive, healthcare, energy, and agriculture, and is
well-positioned to capitalize on emerging waves such as big data. Sections 3.1-3.5 examines AI's future
opportunities.
3.1. Effects of movement and re-establishment
Personnel in stock chains are affected by measurement, equipment use, and the effects of mechanical
technology. The authors in [41], claim that employment replacement is driving this new development,
which has resulted in severe effects on the economy. They claim that unless the extinction effect is
equated with all of the forces of resistance known as the reverberation effect (the creation of new jobs),
the economy's profits will not be sustainable, and monetary reform will not progress.
A productive workforce with high and consistent wages is required for reasonable financial progress.
High unemployment encourages people to spend less, which affects businesses that manufacture goods
and provide a variety of services to workers. What new jobs could be created in such situations to
compensate for the commitment to look for work? AI will almost certainly continue to supplant a few
man-made inventions and computer-assisted attempts, for example confession, decision-making,
communication, and power [41]. In [42], complexity, judgment, relationship-based learning, original
thinking, and a mix of actual labor, empathy, and correspondence [43] present new nonprofit
intelligence agencies. For example, the following are some of the new job titles:
"Trainers" who teach the algorithm,
"Interpreters" who reveal non-technical intelligence decisions, and
"Sustainers" who probe the effectiveness of artificial intelligence.
3.2. AI’s Accountability and Trust
The AI programs currently operates in a short-term unmanaged context, despite its numerous
advantages. An atmosphere of climate change is created when there are no guidelines, and lucrative
commercial organizations are encouraged to speed the design of inventive technologies, while investing
little energy in vital testing to safeguard customers from harm and diverse litigations [44]. If the
269
consumer is harmed by new equipment, the problematic guide makes it difficult to discover a legal
object or defense from the provider to keep it operational [21]. For example, if a self-regulating vehicle
collides with a fender bender, or if a patient is misdiagnosed due to inappropriate chemical treatment,
how should a researcher handle himself? In this case, it is clear artificial intelligence has its own
shortcomings. To address some of the challenges, some ethical and practical recommendations have
been made in papers for governing design firms and to guide the numerous goods that produce and use
superior technology improvements. The findings stress the significance of explicit local norms, as well
as direct understanding and commitment to the entire artificial technology development network [44].
The majority of ML algorithms, which are primarily based on neural associations, act as cryptic cues,
allowing a large number of iterative statistics in large databases to undo the model's limitations,
eventually collecting or predicting data according to a predetermined task [43], [45]. While algorithm
design can be tested and justified, information enthusiast are usually unable to provide a precise
characterization of artificial intelligence's fundamental functions [43]. To address this issue, the
European Union Parliament passed the General Data Protection Regulation in 2016, which grants
customers the opportunity to challenge a robotic framework's judgment [45], [46].
3.3. Security of Information and Customization
To sustain interest in personalized content and administration, chains must be able to collect, store, and
distribute enormous amounts of personal data, both organized (name, address, and a unique number of
details, for example) and random (posts, tweets, photos, and recordings, etc). To ensure effective storage
and security of information, enterprises must engage considerably in data creation. Because of the
financial implications of the exchange, rather than buying, chain stores frequently reconsider such
abilities in collaboration with new enterprises. New businesses, unlike huge enterprises, frequently
require costly knowledge bases. Nowadays, small start-up organizations are paying major companies
to obtain technological capabilities, just as they are paying huge companies to acquire large data stocks
and computing capacity.
3.4. The Effects of Robotics and AI on Future Jobs
More recently, robotics and AI have reached incredible global cooperation for a variety of objectives.
Robotics' growing popularity and prominence makes living easier [47], [48]. At the same time, when
robots take over all of the jobs in the industrialized sector, there will be a reduction of human labour.
While robots boost efficiency, they also reduce job prospects. All blue-collar jobs have been taken over
by robots. Robots are now making inroads into white-collar occupations. Robots, man-made
companions, can perform low-paying labor during non-social hours, while providing immense comfort
to the globe. Future generations are likely to perceive the robot as a teacher and caregiver after it learns
feelings like compassion and advanced reaction detection [49], [50], [51]. According to [52], since
robots make everyone's life easier and more comfortable, human-robot interaction is gradually gaining
ground.
4. The Rise of AI, Overall Future Outlooks, and New Threats
A facility's cleverness, mechanical technology, and computing efficiency appear to be beyond the
ability to integrate such things as computer hardware, autos, and consumer gadgets, similar to medical
care, marketing, education, criminal equality, housing, and banking [52]. These advancements will
continue to give businesses and customers a wide range of appealing and open environments, such as
increased productivity, increased customization, higher quality, and longer lead times. Nevertheless,
there are obstacles to overcome in life. These issues include, but are not limited to, the division of labor
by human instructors, the simultaneous development of human and robotic safety, the construction of
a framework to ensure robotic systems, and the development of artificial insemination technologies to
build confidence [54].
AI and technological advancements compel huge changes, and institutions and testing organizations
play a critical role by adjusting to these developments. Typically, educational frameworks are created
to foster local technological development. After all, the implant's creativity was once evaluated by
computer programs and a real-life learning environment. AI has gained great value in the recent two
270
decades, regardless of its specifics. The work in [53] proposed that both intellectual and non-intellectual
intelligence systems should evolve beyond science and design constraints to make people more
inclusive. As a result, colleges should take the lead in managing this multi-sectoral field of view, as
businesses are in desperate need of social and specialized skills that can operate in scientific teams made
up of experts from several fields in unison [53]. Workplaces which use AI technologies have altered
they way people think and carry out work allowing employees with a variety of skills to cooperate,
trade data, and produce spectacular ideas and results. It is now time to modify people's attitudes and
ideas regarding AI, ML, and high-tech equipment, beginning with the educational system, particularly
the way examinations are being conducted. Accordingly, AI's rapid evolution has made it a critical
component of business in all industries and the advantages of AI in managing cybersecurity risks are
evident [54].
5. Conclusion
Over the last two decades, AI and ML applications have witnessed unprecedented developments. AI
has improved robotics and automation, which has a significant impact on many aspects of a company,
particularly in healthcare, education, and retail (marketing) services. AI also has an impact on a wide
range of social and industrial sectors, including rational design, medical care, educational service, and
retail (marketing) services, as well as smart urban planning, transportation, and maintenance. In this
paper, a critical review of AI applications, future opportunities, and threats was presented. In addition,
the paper also discusses how AI-enabled technologies are being used in the healthcare, educational, and
retail (marketing) sectors including their threats and opportunities.
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