A Study Of Artificial Intelligence And Machine Learning In Power Sector

THE JOURNAL OF ORIENTAL RESEARCH MADRAS ISSN : 0022-3301 | APRIL 2023 1
A STUDY OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (ML) IN POWER
SECTOR: AN ANALYSIS
BY
Syed Mohd Akbar Rizvi
M.Tech -Final Year (2021-23) batch, Roll no.: 2158242006, Department-Computer science, Engineering,
(AI & ML) Khwaja Moinuddin Chishti Language University, Sitapur Hardoi Bypass Road, Lucknow,
UP, India
Tasleem Jamal
Assistant Professor, Department-Computer science Engineering, Faculty of Engineering &
Technology, Khwaja Moinuddin Chishti Language University, Sitapur Hardoi Bnypass Road, Lucknow,
UP, India
ABSTRACT
The energy sector worldwide faces growing challenges related to rising demand, efficiency, changing
supply and demand patterns, and a lack of analytics needed for optimal management. These challenges are
more acute in emerging market nations. Efficiency issues are particularly problematic, as the prevalence of
informal connections to the power grid means a large amount of power is neither measured nor billed,
resulting in losses as well as greater CO2 emissions, as consumers have little incentive to rationally use
energy they don’t pay for. The power sector in developed nations has already begun to use artificial
intelligence and related technologies that allow for communication between smart grids, smart meters, and
Internet of Things devices. These technologies can help improve power management, efficiency, and
transparency, and increase the use of renewable energy sources. Adaptation and innovation are extremely
important to the manufacturing industry. This Development should lead to sustainable manufacturing using
new technologies. To promote sustainability, smart production requires global perspectives of smart
production application technology. In this regard, thanks to intensive research efforts in the field of artificial
intelligence (AI), a number of AI‐based techniques, such as machine learning, have already been
established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was
to analyze, systematically, the scientific literature relating to the application of artificial intelligence and
machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence
and machine learning are considered the driving force of smart factory revolution. The purpose of this
review was to classify the literature, including publication year, authors, scientific sector, country,
institution, and keywords. The analysis was done using the Web of Science and SCOPUS database.
Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and
AI empirical studies published in the last century was carried out to highlight the evolution of the topic
before and after Industry 4.0 introduction, from 1999 to now. Eighty‐two articles were reviewed and
classified. A first interesting result is the greater number of works published by the USA and the increasing
interest after the birth of Industry 4.0. It is claimed that artificial intelligence is playing an increasing role
in research areas. At present many intelligent machines are replaced or enhanced human capabilities in

2 THE JOURNAL OF ORIENTAL RESEARCH MADRAS [Vol. XCV-XI]
many areas. Artificial Intelligence is the exhibited by machines or software. It has the advantages over the
natural intelligence as it is more permanent, consistent, less expensive, has the ease of duplication and
dissemination, can be documented and can perform certain tasks much faster and better than the human.
Its scientific goal is to understand intelligence by building computer programs that exhibit intelligent
behaviour. This paper presents some back ground and potential of artificial intelligence and its
implementation in various fields. We discuss issues that have not been studied in detail with in the expert
systems setting, yet are crucial for developing theoretical methods and computational architectures for
automated reasons. Artificial Intelligence is a broad field that encompasses various concepts in Information
Technology. This research paper focuses on different technologies in AI and how they apply to improve
the performance of multiple sectors. The purpose of this study is to discuss Artificial Intelligence and its
present and future applications. AI is the foundation of multiple concepts, such as computing, software
creation, and data transmission. The technologies that use AI are machine learning, deep learning, Natural
Language Generation, speech recognition, robotics, and biometric identification. AI applies to many sectors
such as healthcare sectors, assembling and manufacturing industries, business organizations, and in the
automotive industries. AI also has various advantages that make it gain more popularity in many areas. The
AI-powered machine can perform many jobs at once; they are not costly compared to human beings and
are accurate and efficient. AI also encounters multiple problems that undermine its application. AI is prone
to technical difficulties, security snags, data difficulties, and can cause accidents if users fail to understand
the AI system. The increased use of AI has transformed various sectors by boosting the organization's
performance and facilitating data safety.
Keywords: Artificial Intelligence, artificial intelligence; machine learning, Cognitive computing,
Clustering, Decision tree, Fluent, Machine intelligence, CNN, Logic programming.
Received 05 April 2023, Accepted 10 April 2023, Published 15 April 2023

Correspondence Author: Syed Mohd Akbar Rizvi.
Introduction
The future of AI will be innovative, but may not be shared equally.
….Unknown
Artificial Intelligence (AI) is a science and a set of computational technologies that are inspired by—but
typically operate quite differently from—the ways people use their nervous systems and bodies to sense,
learn, reason, and take action. While concepts already date back more than 50 years, only recently have
technological advances enabled successful implementation at industrial scale.1
Earlier in 1950’s artificial
intelligence success were limited to the scientific field but in the last years, established IT giants like
Google, IBM and nVIDIA – fuelled by the abundance of data, algorithmic advances, and the usages of high
1
Bresnahan, T. and M. Trajtenberg (1995) “General Purpose Technologies ‘Engines of Growth’?” Journal of Econometrics, 65
(1995) 83-108.

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Syed Mohd Akbar Rizvi, Tasleem Jamal
performance hardware for parallel processing have been bridging the gap between science and business
applications.2
The research is designed to address three intended audiences. For the general public, it aims to provide an
accessible, scientifically and technologically accurate portrayal of the current state of AI and its potential.3
For industry, the report describes relevant technologies and legal and ethical challenges, and may help
guide resource allocation. The report is also directed to local, national, and international governments to
help them better plan for AI in governance.
WHAT IS ARTIFICIAL INTELLIGENCE?
DEFINITION
Curiously, the lack of a precise, universally accepted definition of AI probably has helped the field to grow,
blossom, and advance at an ever-accelerating pace. Practitioners, researchers, and developers of AI are
instead guided by a rough sense of direction and an imperative to “get on with it.” Still, a definition remains
important and Nils J. Nilsson has provided a useful one:
“Artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that
quality that enables an entity to function appropriately and with foresight in its environment.” From this
perspective, characterizing AI depends on the credit one is willing to give synthesized software and
hardware for functioning “appropriately” and with “foresight.” A simple electronic calculator performs
calculations much faster than the human brain, and almost never makes a mistake.
ARTIFICIAL INTELLIGENCE IN THE POWER SECTOR
The use of AI in the power sector is now reaching emerging markets, where it may have a critical impact,
as clean, cheap, and reliable energy is essential to development. The challenges can be addressed over time
by transferring knowledge of the power sector to AI
Software companies. When designed carefully, AI systems can be particularly useful in the automation of
routine and structured tasks, leaving humans to grapple with the power challenges of tomorrow. Access to
energy is at the very heart of development. Therefore, a lack of energy access which is the reality for one
billion people, mostly in Sub-Saharan Africa and South Asia is a fundamental impediment to progress, one
that has an impact on health, education, food security, gender equality, livelihoods, and poverty reduction.
Universal access to affordable, reliable, and sustainable modern energy is one of the Sustainable
Development Goals (SDGs).4
Yet it will remain just that—a goal—unless innovative solutions and modern
technologies can overcome the many energy-related obstacles that plague emerging markets, from a lack
of sufficient power generation, to poor transmission and distribution infrastructure, to affordability and
climate concerns. In addition, the diversification and decentralization of energy production, along with the
advent of new technologies and changing demand patterns, create complex challenges for power
generation, transmission, distribution, and consumption in all nations. Artificial intelligence, or AI, has the
2
Aghion, P. and P. Howitt (1992) “A Model of Growth Through Creative Destruction,” Econometrica, 60(2), 323-251.
3
Bresnahan, T., E. Brynjolfsson, and L. Hitt (2002) “Information Technology, Workplace Organization, and the Demand for
Skilled Labor: Firm-Level Evidence,” The Quarterly Journal of Economics, 117(1), 339-376.
4
Vinuesa, Ricardo, Hossein Azizpour Hossein, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna
Felländer, Simone Langhans, Max Tegmark, and Francesco Fuso Nerini. 2020. “The Role of Artificial Intelligence in
Achieving the Sustainable Development Goals.” https://arxiv.org/ ftp/arxiv/papers/1905/1905.00501.pdf.

potential to cut energy waste, lower energy costs, and facilitate and accelerate the use of clean renewable
energy sources in power grids worldwide. AI can also improve the planning, operation, and control of
power systems.5
Thus, AI technologies are closely tied to the ability to provide clean and cheap energy that
is essential to development. For the purposes of this note, we follow the definitions and descriptions of
basic, advanced, and autonomous artificial
intelligence that were put forward in EM Compass Note 69. AI refers to the science and engineering of
making machines intelligent, especially intelligent computer programs. AI in this note is a series of
approaches, methods, and technologies that display intelligent behavior by analyzing their environments
and taking actions—with some degree of autonomy to achieve specific targets in energy.
TOWARD A SMART POWER SECTOR
The power sector has a promising future with the advent of solutions such as AI-managed smart grids.
These are electrical grids that allow two-way communication between utilities and consumers.6
Smart grids
are embedded with an information layer that allows communication between its various components so
they can better respond to quick changes in energy demand or urgent situations. This information layer,
created through widespread installation of smart meters and sensors, allows for data collection, storage,
and analysis. Phas or measurement units (PMUs), or synchrophasors, are another essential element of the
modern smart grid. They enable real-time measurement and alignment of data from multiple remote points
across the grid. This creates a current, precise, and integrated view of the entire power system, facilitating
better grid management. Paired with powerful data analytics, these smart-grid elements have helped
improve the reliability, security, and efficiency of electricity transmission and distribution networks. Given
the large volume and diverse structures of such data, AI techniques such as machine learning are best suited
for their analysis and use.6 This data analysis can be used for a variety of purposes, including fault
detection, predictive maintenance, power quality monitoring, and renewable energy forecasting. Innovation
in information and communications technologies (ICT), cloud computing, big-data analytics, and artificial
intelligence have supported the proliferation of smart metering. The widespread use of smart meters and
advanced sensor technology has created huge amounts of data that is generated rapidly. This data requires
new methods for storage, transfer, and analysis. For illustration sake, with a sampling rate of four times per
hour, one million smart meters installed in a smart grid would generate over 35 billion records. The use of
smart grids in EM countries lags advanced economies, but several EM countries have taken steps to adopt
them, with various level of development. These include Brazil, China, Gulf Cooperation Council (GCC)
countries, Malaysia, South Africa, Thailand, and Vietnam among others. Deep learning techniques, a subset
of machine learning, can help discern patterns and anomalies across very large datasets both on the power
demand and power supply sides that otherwise would be nearly impossible to achieve.7
This has resulted
in improved systems, faster problem solving, and better performance. Advanced economies are leading the
way in the application of AI in the power sector. For example, Deep Mind, a subsidiary of Google, has
been applying machine learning algorithms to 700 megawatts of wind power in the central United States
5
Wehus, Walter Norman. 2017. “Soon, Artificial Intelligence Can Operate Hydropower.” University of Agder, January 20,
2017. https://www.uia.no/en/ news/soonartificial-intelligence-can-operate-hydropower-plants.
6
Much of this paragraph was informed by Rastgoufard, Samin. 2018. “Applications of Artificial Intelligence in Power
Systems.” University of New Orleans Theses and Dissertation, No. 2487. May 18, 2018. https://scholarworks.uno.edu/td/2487
7
Gagan, Olivia. 2018. “Here’s How AI Fits Into the Future of Energy.” www. weforum.com, May 25, 2018.
https://www.weforum.org/agenda/2018/05/ how-ai-can-help-meet-global-energy-demand.

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to predict power output 36 hours ahead of actual generation using neural networks trained on weather
forecasts and historical wind turbine data. Deep learning algorithms are also able to learn on their own.
When applied to energy data patterns, the algorithms learn by trial and error. For example, in Norway,
Agder Energi partnered with the University of Agder to develop an algorithm to optimize water usage in
hydropower plants.
Water may appear to be a seemingly endless source of energy, however only a limited amount of it is
available to produce hydroelectricity, so it must be used optimally. In Canada, Sentient Energy, a leading
provider of advanced grid monitoring and analytics solutions to electric utilities, was selected in 2017 to
support power and natural gas utility Manitoba Hydro. Its Worst Feeder Program initiative is anticipated
to allow Manitoba Hydro to speed up system
fault identification and restore power to customers faster at the most critical points on its distribution grid.
AI can also help with prediction issues in hydroelectricity production. In general, most countries do have
reliable hydrology data collected over a 40 years period, and
in some cases, longer, that facilitates the prediction of hydrology using proven stochastic dual dynamic
programing tools. However, in the past year climate change has disrupted such predictions. Currently, the
mathematical models underlying the operation of power production are approximately 30 years old and are
generally incompatible with the current realities of the hydro power sector. The increasing uncertainty of
parameters such as future
precipitation levels or pricing are among the many challenges to optimizing production and profit.8
AI APPLICATIONS IN THE POWER SECTOR
Fault prediction has been one of the major applications of artificial intelligence in the energy sector, along
with real time maintenance and identification of ideal maintenance schedules. In an industry where
equipment failure is common, with potentially significant consequences, AI combined with appropriate
sensors can be useful to monitor equipment and detect failures before they happen, thus saving resources,
money, time, and lives. Geothermal energy, which yields steady energy output, is being discussed as a
potential source of base load power (the minimum amount of power needed to be supplied to the electrical
grid at any given time) to support the expansion of less reliable renewables. Toshiba ESS has been
conducting research on the use of IoT and AI to improve the efficiency and reliability of geothermal power
plants. For example, predictive diagnostics enabled by rich data are used to predict problems that could
potentially shut down plants. Preventive measures such as chemical agent sprays to avoid turbine
shutdowns are optimized (quantity, composition, and timing) using IoT and AI. Such innovations are
important in a country like Japan, which has the third largest geothermal resources in the world, especially
in the face of decreasing costs of competing renewable sources such as solar power.9
APPLICATION OF ARTIFICIAL INTELLIGENCE IN VARIOUS FIELDS
❖ TRANSPORTATION
8
Chandra, Harsh. 2019. “Artificial Intelligence (AI) vs Machine Learning (ML) vs Big Data.” May 10, 2019.
https://heartbeat.fritz.ai/artificialintelligence- ai-vs-machine-learning-ml-vs-big-data-909906eb6a92.
9
Zhang Zhen. 2011. “Smart Grid in America and Europe: Similar Desires, Different Approaches.” Public Utilities
Fortnightly, Vol. 149, No. 1, January 1,
2011. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1799705.

Transportation is likely to be one of the first domains in which the general public will be asked to trust the
reliability and safety of an AI system for a critical task. Autonomous transportation will soon be
commonplace and, as most people’s first experience with physically embodied AI systems, will strongly
influence the public’s perception of AI.10
Once the physical hardware is made sufficiently safe and robust,
its introduction to daily life may happen so suddenly as to surprise the public, which will require time to
adjust. As cars will become better drivers than people, city-dwellers will own fewer cars, live further from
work, and spend time differently, leading to an entirely new urban organization. Further, in the typical
North American city in 2030, changes won’t be limited to cars and trucks, but are likely to include flying
vehicles and personal robots, and will raise social, ethical and policy issues. A few key technologies have
already catalysed the widespread adoption of AI in transportation. Compared to 2000, the scale and
diversity of data about personal and population-level transportation available today—enabled by the
adoption of smartphones and decreased costs and improved accuracies for variety of sensors—is
astounding. Without the availability of this data and connectivity, applications such as real-time sensing
and prediction of traffic, route calculations, peer-to-peer ridesharing and self-driving cars would not be
possible.
FOR EXAMPLE: smart cars, self-driving vehicles, on demand transportation, etc.
❖ HOME/SERVICE ROBOTS
Robots have entered people’s homes in the past fifteen years. Disappointingly slow growth in the diversity
of applications has occurred simultaneously with increasingly sophisticated AI deployed on existing
applications. AI advances are often inspired by mechanical innovations, which in turn prompt new AI
techniques to be introduced. Over the next fifteen years, coincident advances in mechanical and AI
technologies promise to increase the safe and reliable use and utility of home robots in a typical North
American city.11
Special purpose robots will deliver packages, clean offices, and enhance security, but
technical constraints and the high costs of reliable mechanical devices will continue to limit commercial
opportunities to narrowly defined applications for the foreseeable future. As with self-driving cars and
other new transportation machines, the difficulty of creating reliable, market-ready hardware is not to be
underestimated.
FOR EXAMPLE: Vacuum cleaners, home robots 2030, etc.
❖ HEALTHCARE
For AI technologies, healthcare has long been viewed as a promising domain. AI-based applications could
improve health outcomes and quality of life for millions of people in the coming years—but only if they
gain the trust of doctors, nurses, and patients, and if policy, regulatory, and commercial obstacles are
removed. Prime applications include clinical decision support, patient monitoring and coaching, automated
devices to assist in surgery or patient care, and management of healthcare systems. Recent successes, such
as mining social media to infer possible health risks, machine learning to predict patients at risk, and
robotics to support surgery, have expanded a sense of possibility for AI in healthcare. Improvements in
methods for interacting with medical professionals and patients will be a critical challenge. As in other
10
Brooks, R. (1990) “Elephants Don’t Play Chess,” Robotics and Autonomous Systems, 6, 3-15.
11
Brynjolfsson, E. and K. McElheran (2017) “The Rapid Adoption of Data-Driven Decision-Making,”American Economic
Review, 106(5), 133-139

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domains, data is a key enabler.12
There has been an immense forward leap in collecting useful data from
personal monitoring devices and mobile apps, from electronic health records (EHR) in clinical settings and,
to a lesser extent, from robots designed to assist with medical procedures and hospital operations. But using
this data to enable more finely-grained diagnostics and treatments for both individual patients and patient
populations has proved difficult. Research and deployment have been slowed by outdated regulations and
incentive structures. Poor human-computer interaction methods and the inherent difficulties and risks of
implementing technologies in such a large and complex system have slowed realization of AI’s promise in
healthcare.61The reduction or removal of these obstacles, combined with innovations still on the horizon,
have the potential to significantly improve health outcomes and quality of life for millions of people in the
coming years.
FOR EXAMPLE: The clinical setting, healthcare analytics, healthcare robotics, mobile health, elder care,
etc.
❖ EDUCATION
The past fifteen years have seen considerable AI advances in education. Applications are in wide use by
educators and learners today, with some variation between K-12 and university settings. Though quality
education will always require active engagement by human teachers, AI promises to enhance education at
all levels, especially by providing personalization at scale. Similar to healthcare, resolving how to best
integrate human interaction and face-to-face learning with promising AI technologies remains a key
challenge. Robots have long been popular educational devices, starting with the early Lego Mind storms
kits developed with the MIT Media Lab in the 1980s. Intelligent Tutoring Systems (ITS) for science, math,
language, and other disciplines match students with interactive machine tutors.13
Natural Language
Processing, especially when combined with machine learning and crowdsourcing, has boosted online
learning and enabled teachers to multiply the size of their classrooms while simultaneously addressing
individual students’ learning needs and styles. The data sets from large online learning systems have fuelled
rapid growth in learning analytics. Still, schools and universities have been slow in adopting AI
technologies primarily due to lack of funds and lack of solid evidence that they help students achieve
learning objectives. Over the next fifteen years in a typical North American city, the use of intelligent tutors
and other AI technologies to assist teachers in the classroom and in the home is likely to expand
significantly, as will learning based on virtual reality applications. But computer-based learning systems
are not likely to fully replace human teaching in schools.
❖ PUBLIC SAFETY AND SECURITY
Cities already have begun to deploy AI technologies for public safety and security. By 2030, the typical
North American city will rely heavily upon them. These include cameras for surveillance that can detect
anomalies pointing to a possible crime, drones, and predictive policing applications. As with most issues,
there are benefits and risks. Gaining public trust is crucial. While there are legitimate concerns that policing
that incorporates AI may become overbearing or pervasive in some contexts, the opposite is also possible.
12
Griliches, Z. (1957) “Hybrid Corn: An Exploration in the Economics of Technological Change,”Econometrica, 25(4), 501-
522.
13
Henderson, R. and K. Clark (1990) “Architectural Innovation: The Reconfiguration of Existing Product
Technologies and the Failure of Established Firms,” Administrative Science Quarterly, 35(1), 9-30.

AI may enable policing to become more targeted and used only when needed. And assuming careful
deployment, AI may also help remove some of the bias inherent in human decision-making. One of the
more successful uses of AI analytics is in detecting white collar crime, such as credit card fraud. Cyber
security (including spam) is a widely shared concern, and machine learning is making an impact. AI tools
may also prove useful in helping police manage crime scenes or search and rescue events by helping
commanders prioritize tasks and allocate resources, though these tools are not yet ready for automating
such activities.14
Improvements in machine learning in general, and transfer learning in particular—for
speeding up learning in new scenarios based on similarities with past scenarios—may facilitate such
systems. The cameras deployed almost everywhere in the world today tend to be more useful for helping
solve crimes than preventing them.102 103 This is due to the low quality of event identification from videos
and the lack of manpower to look at massive video streams. As AI for this domain improves, it will better
assist crime prevention and prosecution through greater accuracy of event classification and efficient
automatic processing of video to detect anomalies—including, potentially, evidence of police malpractice.
These improvements could lead to even more widespread surveillance. Some cities have already added
drones for surveillance purposes, and police use of drones to maintain security of ports, airports, coastal
areas, waterways, and industrial facilities is likely to increase, raising concerns about privacy, safety, and
other issues.15
❖ EMPLOYMENT AND WORKPLACE
While AI technologies are likely to have a profound future impact on employment and workplace trends in
a typical North American city, it is difficult to accurately assess current impacts, positive or negative. In
the past fifteen years, employment has shifted due to a major recession and increasing globalization,
particularly with China’s introduction to the world economy, as well as enormous changes in non-AI digital
technology. Since the 1990s, the US has experienced continued growth in productivity and GDP, but
median income has stagnated and the employment to population ratio has fallen. There are clear examples
of industries in which digital technologies have had profound impacts, good and bad, and other sectors in
which automation will likely make major changes in the near future. Many of these changes have been
driven strongly by “routine” digital technologies, including enterprise resource planning, networking,
information processing, and search. Understanding these changes should provide insights into how AI will
affect future labour demand, including the shift in skill demands. To date, digital technologies have been
affecting workers more in the skilled middle, such as travel agents, rather than the very lowest-skilled or
highest skilled work.110 On the other hand, the spectrum of tasks that digital systems can do is evolving
as AI systems improve, which is likely to gradually increase the scope of what is considered routine. AI is
also creeping into high end of the spectrum, including professional services not historically performed by
machines. To be successful, AI innovations will need to overcome understandable human fears of being
marginalized. AI will likely replace tasks rather than jobs in the near term, and will also create new kinds
of jobs. But the new jobs that will emerge are harder to imagine in advance than the existing jobs that will
likely be lost. Changes in employment usually happen gradually, often without a sharp transition, a trend
14
Krizhevsky, A., I. Sutskever, G. Hinton (2012) “ImageNet Classification with Deep Convolutional NeuralNetworks,”
Advances in Neural Information Processing, 25, MIT Press.
15
Leung, M.K.K., A. Delong, B. Alipanahi, and B.J. Frey (2016) “Machine Learning in Genomic Medicine: A Review of
Computational Problems and Data Sets,” Proceedings of the IEEE, 104(1): 176-197.

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likely to continue as AI slowly moves into the workplace.16
A spectrum of effects will emerge, ranging
from small amounts of replacement or augmentation to complete replacement. For example, although most
of a lawyer’s job is not yet automated, AI applied to legal information extraction and topic modelling has
automated parts of first-year lawyers’ jobs. In the not too distant future, a diverse array of job-holders, from
radiologists to truck drivers to gardeners, may be affected. AI may also influence the size and location of
the workforce. Many organizations and institutions are large because they perform functions that can be
scaled only by adding human labour, either “horizontally” across geographical areas or “vertically” in
management hierarchies. As AI takes over many functions, scalability no longer implies large
organizations. Many have noted the small number of employees of some high profile internet companies,
but not of others. There may be a natural scale of human enterprise, perhaps where the CEO can know
everyone in the company. Through the creation of efficiently outsourced labour markets enabled by AI,
enterprises may tend towards that natural size. AI will also create jobs, especially in some sectors, by
making certain tasks more important, and create new categories of employment by making new modes of
interaction possible.
❖ ENTERTAINMENT
With the explosive growth of the internet over the past fifteen years, few can imagine their daily lives
without it. Powered by AI, the internet has established user-generated content as a viable source of
information and entertainment. Social networks such as Facebook are now pervasive, and they function as
personalized channels of social interaction and entertainment sometimes to the detriment of interpersonal
interaction. Apps such as WhatsApp and Snap chat enable smart-phone users to remain constantly “in
touch” with peers and share sources of entertainment and information. In on-line communities such as
Second Life and role-playing games such as World of War craft, people imagine an alternative existence
in a virtual world. Specialized devices, such as Amazon’s Kindle have also redefined the essentials of long-
cherished pastimes. Books can now be browsed and procured with a few swipes of the finger, stored by the
thousands in a pocket-sized device, and read in much the same way as a handheld paperback. Trusted
platforms now exist for sharing and browsing blogs, videos, photos, and topical discussions, in addition to
a variety of other user-generated information. To operate at the scale of the internet, these platforms must
rely on techniques that are being actively developed in natural language processing, information retrieval,
image processing, crowdsourcing, and machine learning17
. Algorithms such as collaborative filtering have
been developed, for example, to recommend relevant movies, songs, or articles based on the user’s
demographic details and browsing history. Traditional sources of entertainment have also embraced AI to
keep pace with the times. As exemplified in the book and movie Money ball, professional sport is now
subjected to intensive quantitative analysis. Beyond aggregate performance statistics, on-field signals can
be monitored using sophisticated sensors and cameras. Software has been created for composing music and
recognizing soundtracks. Techniques from computer vision and NLP have been used in creating stage
16
Marco, A., A. Myers, S. Graham, P. D’Agostino, and K. Apple (2015) “The USPTO Patent Assignment Dataset: Descriptions
and Analysis,” USPTO Working Paper No. 2015-02, 1-53.
17
Marco, A., M. Carley, S. Jackson and A. Myers (2015) “The USPTO Historical Patent Data Files,” USPTO Working Paper
No. 2015-01, 1-57.

performances. Even the lay user can exercise his or her creativity on platforms such as Words Eye, which
automatically generates 3D scenes from natural language text. AI has also come to the aid of historical
research in the arts, and is used extensively in stylometry and, more recently, in the analysis of paintings.
The enthusiasm with which humans have responded to AI-driven entertainment has been surprising and
led to concerns that it reduces interpersonal interaction among human beings. Few predicted that people
would spend hours on end interacting with a display.18
Children often appear to be genuinely happier
playing at home on their devices rather than outside with their friends. AI will increasingly enable
entertainment that is more interactive, personalized, and engaging. Research should be directed toward
understanding how to leverage these attributes for individuals’ and society’s benefit.
❖ FINANCE AND ECONOMICS
Financial institutions have long used artificial neural network systems to detect charges or claims outside
of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987
when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the
unauthorised use of debit cards. Programs like Kasisto and Money stream are using AI in financial
services.19
Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in
stocks, and manage properties. AI can react to changes overnight or when business is not taking place. In
August 2001, robots beat humans in a simulated financial trading competition. AI has also reduced fraud
and financial crimes by monitoring behavioural patterns of users for any abnormal changes or anomalies.
The use of AI machines in the market in applications such as online trading and decision making has
changed major economic theories. For example, AI based buying and selling platforms have changed the
law of supply and demand in that it is now possible to easily estimate individualized demand and supply
curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the
market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in
the markets limits the consequences of behaviour in the markets again making markets more efficient.
Other theories where AI has had impact include in rational choice, rational expectations, game
theory, Lewis turning point, portfolio optimization and counterfactual thinking.
❖ VIDEO GAMES
In video games, artificial intelligence is routinely used to generate dynamic purposeful behaviour in non-
player characters (NPCs). In addition, well-understood AI techniques are routinely used for path finding.
Some researchers consider NPC AI in games to be a "solved problem" for most production tasks. Games
with more atypical AI include the AI director of Left 4 Dead (2008) and the neuro-evolutionary training of
platoons in Supreme Commander 2 (2010).
AFTERMATH
BENEFITS OF ARTIFICIAL INTELLIGENCE
Companies new to the space can learn a great deal from early adopters who have invested billions into AI and
are now beginning to reap a range of benefits.
18
Minsky, M. (1961) “Steps Toward Artificial Intelligence,” Proceedings of the IRE, 8-30.
Mokyr, J (2002) Gifts of Athena, Princeton University Press.
19
Nilsson, N. (2010), The Quest for Artificial Intelligence: A History of Ideas and Achievements, Cambridge University Press.

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After decades of extravagant promises and frustrating disappointments, artificial intelligence (AI) is finally
starting to deliver real-life benefits to early-adopting companies. Retailers on the digital frontier rely on AI-
powered robots to run their warehouses—and even to automatically order stock when inventory runs low.
Utilities use AI to forecast electricity demand. Automakers harness the technology in self-driving cars.20
A confluence of developments is driving this new wave of AI development. Computer power is growing,
algorithms and AI models are becoming more sophisticated, and, perhaps most important of all, the world is
generating once-unimaginable volumes of the fuel that powers AI data. Billions of gigabytes every day, collected
by networked devices ranging from web browsers to turbine sensors.
The entrepreneurial activity unleashed by these developments drew three times as much investment in 2016
between $26 billion and $39 billion as it did three years earlier. Most of the investment in AI consists of internal
R&D spending by large, cash-rich digital-native companies like Amazon, Baidu, and Google.
However, early evidence suggests that there is a business case to be made, and that AI can deliver real value to
companies willing to use it across operations and within their core functions.
• A company program with artificial intelligence can answer the generic question it is meant to solve.
• Artificial intelligence can absorb new modifications by putting highly independent pieces of even a
minute piece of information of programme without affecting its structure.
• Quick and easy modification of programmes.
The statistic shows the growth of the artificial intelligence market worldwide, from 2017 to 2025. In 2017,
the global AI market is expected to grow approximately 175% from 2016 levels, reaching an forecast size
of 2.4 billion U.S. dollars. Artificial intelligence is a term used to describe a variety of technologies. These
include machine learning, computer vision, natural language processing (NLP), and machine reasoning,
among others. Artificial intelligence is expected to have implications for and a use in every industry
20
Romer, P. (1990) “Endogenous Technological Change,” Journal of Political Economy, 98(5), S71-S102.
Rosenberg, N. and M. Trajtenberg (2004) “A General Purpose Technology at Work: The Corliss Steam
Engine in the Late-Nineteenth-Century United States,” Journal of Economic History, 61(1), 61-99.

vertical, and is likely to be one of the next great technological shifts, like the advent of the computer age
or the smartphone revolution.
HOW CAN AI BE DANGEROUS?
Most researchers agree that a super intelligent AI is unlikely to exhibit human emotions like love or hate,
and that there is no reason to expect AI to become intentionally benevolent or malevolent. Instead, when
considering how AI might become a risk, experts think two scenarios most likely:
1. The AI is programmed to do something devastating: Autonomous weapons are artificial
intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons
could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war
that also results in mass casualties. To avoid being thwarted by the enemy, these weapons would
be designed to be extremely difficult to simply “turn off,” so humans could plausibly lose control of
such a situation. This risk is one that’s present even with narrow AI, but grows as levels of AI
intelligence and autonomy increase.
2. The AI is programmed to do something beneficial, but it develops a destructive method for
achieving its goal: This can happen whenever we fail to fully align the AI’s goals with ours, which is
strikingly difficult. If you ask an obedient intelligent car to take you to the airport as fast as possible,
it might get you there chased by helicopters and covered in vomit, doing not what you wanted but
literally what you asked for. If a super intelligent system is tasked with a ambitious geo-engineering
project, it might wreak havoc with our ecosystem as a side effect, and view human attempts to stop it
as a threat to be met.
As these examples illustrate, the concern about advanced AI isn’t malevolence but competence. A super-
intelligent AI will be extremely good at accomplishing its goals, and if those goals aren’t aligned with ours,
we have a problem. You’re probably not an evil ant-hater who steps on ants out of malice, but if you’re in
charge of a hydroelectric green energy project and there’s an anthill in the region to be flooded, too bad for
the ants. A key goal of AI safety research is to never place humanity in the position of those ants.21
Artificial Intelligence and the Future of Humans
Digital life is augmenting human capacities and disrupting eons-old human activities. Code-driven systems
have spread to more than half of the world’s inhabitants in ambient information and connectivity, offering
previously unimagined opportunities and unprecedented threats. As emerging algorithm-driven artificial
intelligence (AI) continues to spread, will people be better off than they are today?
Some 979 technology pioneers, innovators, developers, business and policy leaders, researchers and
activists answered this question in a canvassing of experts conducted in the summer of 2018. The experts
predicted networked artificial intelligence will amplify human effectiveness but also threaten human
autonomy, agency and capabilities. They spoke of the wide-ranging possibilities; that computers might
match or even exceed human intelligence and capabilities on tasks such as complex decision-making,
reasoning and learning, sophisticated analytics and pattern recognition, visual acuity, speech recognition
21
Rumelhart, D., G. Hinton, and R. Williams (1986) “Learning Internal Representations by ErrorPropagation,” in J. McClelland
and D. Rumelhart (editors), Parallel Distributed Processing:Explorations in the Microstructure of Cognition, Volume 2:
Psychological and Biological Models, MIT Press, 7-57.

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and language translation. They said “smart” systems in communities, in vehicles, in buildings and utilities,
on farms and in business processes will save time, money and lives and offer opportunities for individuals
to enjoy a more-customized future.
Many focused their optimistic remarks on health care and the many possible applications of AI in
diagnosing and treating patients or helping senior citizens live fuller and healthier lives. They were also
enthusiastic about AI’s role in contributing to broad public-health programs built around massive amounts
of data that may be captured in the coming years about everything from personal genomes to nutrition.
Additionally, a number of these experts predicted that AI would abet long-anticipated changes in formal
and informal education systems.22
Yet, most experts, regardless of whether they are optimistic or not, expressed concerns about the long-term
impact of these new tools on the essential elements of being human. All respondents in this non-scientific
canvassing were asked to elaborate on why they felt AI would leave people better off or not. Many shared
deep worries, and many also suggested pathways toward solutions. The main themes they sounded about
threats and remedies are outlined in the accompanying table.
Advantages & Disadvantages of Artificial Intelligence
An artificial intelligence program is a program that is capable of learning and thinking. It is possible to
consider anything to be artificial intelligence if it consists of a program performing a task that we would
normally assume a human would perform. Let's begin with the advantages of artificial intelligence.23
Advantages of Artificial Intelligence
1. Reduction in Human Error
One of the biggest advantages of Artificial Intelligence is that it can significantly reduce errors and increase
accuracy and precision. The decisions taken by AI in every step is decided by information previously
gathered and a certain set of algorithms. When programmed properly, these errors can be reduced to null.
2. Zero Risks
Another big advantage of AI is that humans can overcome many risks by letting AI robots do them for us.
Whether it be defusing a bomb, going to space, exploring the deepest parts of oceans, machines with metal
bodies are resistant in nature and can survive unfriendly atmospheres. Moreover, they can provide accurate
work with greater responsibility and not wear out easily.
3. 24x7 Availability
There are many studies that show humans are productive only about 3 to 4 hours in a day. Humans also
need breaks and time offs to balance their work life and personal life. But AI can work endlessly without
breaks. They think much faster than humans and perform multiple tasks at a time with accurate results.
They can even handle tedious repetitive jobs easily with the help of AI algorithms.
22
Scotchmer, S. (1991) “Standing on the Shoulders of Giants: Cumulative Research and the Patent Law,”Journal of Economic
Perspectives, 5(1), 29-41.
23
Williams, H. .(2013) “Intellectual Property Rights and Innovation: Evidence from the Human Genome,”,Journal of Political
Economy, 121(1): 1-27

4. Digital Assistance
Some of the most technologically advanced companies engage with users using digital assistants, which
eliminates the need for human personnel. Many websites utilize digital assistants to deliver user-requested
content. We can discuss our search with them in conversation. Some chatbots are built in a way that makes
it difficult to tell whether we are conversing with a human or a chatbot.
We all know that businesses have a customer service crew that must address the doubts and concerns of
the patrons. Businesses can create a chatbot or voice bot that can answer all of their clients' questions using
AI.
5. New Inventions
In practically every field, AI is the driving force behind numerous innovations that will aid humans in
resolving the majority of challenging issues.
For instance, recent advances in AI-based technologies have allowed doctors to detect breast cancer in a
woman at an earlier stage.
6. Unbiased Decisions
Human beings are driven by emotions, whether we like it or not. AI on the other hand, is devoid of emotions
and highly practical and rational in its approach. A huge advantage of Artificial Intelligence is that it doesn't
have any biased views, which ensures more accurate decision-making.
7. Perform Repetitive Jobs
We will be doing a lot of repetitive tasks as part of our daily work, such as checking documents for flaws
and mailing thank-you notes, among other things. We may use artificial intelligence to efficiently automate
these menial chores and even eliminate "boring" tasks for people, allowing them to focus on being more
creative.
Example: In banks, it's common to see multiple document checks to obtain a loan, which is a time-
consuming task for the bank's owner. The owner can expedite the document verification process for the
advantage of both the clients and the owner by using AI Cognitive Automation.24
8. Daily Applications
Today, our everyday lives are entirely dependent on mobile devices and the internet. We utilize a variety
of apps, including Google Maps, Alexa, Siri, Cortana on Windows, OK Google, taking selfies, making
calls, responding to emails, etc. With the use of various AI-based techniques, we can also anticipate today’s
weather and the days ahead.
Example: About 20 years ago, you must have asked someone who had already been there for instructions
when you were planning a trip. All you need to do now is ask Google where Bangalore is. The best route
between you and Bangalore will be displayed, along with Bangalore's location, on a Google map.25
9. AI in Risky Situations
One of the main benefits of artificial intelligence is this. By creating an AI robot that can perform perilous
tasks on our behalf, we can get beyond many of the dangerous restrictions that humans face. It can be
24
Prediction in Structure-based Drug Discovery.” arXiv:1510.02855 [cs.LG]
25
Turing, A. (1950) “Computing Machinery and Intelligence,” Mind, 59, 433-460.
Wallach, I. Dzamba, M. and Heifels, A. “AtomNet: A Deep Convolutional Neural Network for Bioactivity

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utilized effectively in any type of natural or man-made calamity, whether it be going to Mars, defusing a
bomb, exploring the deepest regions of the oceans, or mining for coal and oil.
For instance, the explosion at the Chernobyl nuclear power facility in Ukraine. As any person who came
close to the core would have perished in a matter of minutes, at the time, there were no AI-powered robots
that could assist us in reducing the effects of radiation by controlling the fire in its early phases.
10. Faster decision-making
Faster decision-making is another benefit of AI. By automating certain tasks and providing real-time
insights, AI can help organizations make faster and more informed decisions. This can be particularly
valuable in high-stakes environments, where decisions must be made quickly and accurately to prevent
costly errors or save lives.
11. Pattern identification
Pattern identification is another area where AI excels. With its ability to analyze vast amounts of data and
identify patterns and trends, AI can help businesses and organizations better understand customer behavior,
market trends, and other important factors. This information can be used to make better decisions and
improve business outcomes.
12. Medical Applications
AI has also made significant contributions to the field of medicine, with applications ranging from
diagnosis and treatment to drug discovery and clinical trials. AI-powered tools can help doctors and
researchers analyze patient data, identify potential health risks, and develop personalized treatment plans.
This can lead to better health outcomes for patients and help accelerate the development of new medical
treatments and technologies.26
Let us now look at what are the main disadvantages that Artificial intelligence holds.
Disadvantages of Artificial Intelligence
1. High Costs
The ability to create a machine that can simulate human intelligence is no small feat. It requires plenty of
time and resources and can cost a huge deal of money. AI also needs to operate on the latest hardware and
software to stay updated and meet the latest requirements, thus making it quite costly.
2. No creativity
A big disadvantage of AI is that it cannot learn to think outside the box. AI is capable of learning over time
with pre-fed data and past experiences, but cannot be creative in its approach. A classic example is the bot
Quill who can write Forbes earning reports. These reports only contain data and facts already provided to
the bot. Although it is impressive that a bot can write an article on its own, it lacks the human touch present
in other Forbes articles.
3. Unemployment
One application of artificial intelligence is a robot, which is displacing occupations and increasing
unemployment (in a few cases). Therefore, some claim that there is always a chance of unemployment as
a result of chatbots and robots replacing humans.
26
Wallach, I. Dzamba, M. and Heifels, A. “AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in
Structure-based Drug Discovery.” arXiv:1510.02855 [cs.LG]

For instance, robots are frequently utilized to replace human resources in manufacturing businesses in some
more technologically advanced nations like Japan. This is not always the case, though, as it creates
additional opportunities for humans to work while also replacing humans in order to increase efficiency.
4. Make Humans Lazy
AI applications automate the majority of tedious and repetitive tasks. Since we do not have to memorize
things or solve puzzles to get the job done, we tend to use our brains less and less. This addiction to AI can
cause problems to future generations.
5. No Ethics
Ethics and morality are important human features that can be difficult to incorporate into an AI. The rapid
progress of AI has raised a number of concerns that one day, AI will grow uncontrollably, and eventually
wipe out humanity. This moment is referred to as the AI singularity.
6. Emotionless
Since early childhood, we have been taught that neither computers nor other machines have feelings.
Humans function as a team, and team management is essential for achieving goals. However, there is no
denying that robots are superior to humans when functioning effectively, but it is also true that human
connections, which form the basis of teams, cannot be replaced by computers.
7. No Improvement
Humans cannot develop artificial intelligence because it is a technology based on pre-loaded facts and
experience. AI is proficient at repeatedly carrying out the same task, but if we want any adjustments or
improvements, we must manually alter the codes. AI cannot be accessed and utilized akin to human
intelligence, but it can store infinite data.
Machines can only complete tasks they have been developed or programmed for; if they are asked to
complete anything else, they frequently fail or provide useless results, which can have significant negative
effects. Thus, we are unable to make anything conventional.
CONCLUTION
Artificial intelligence exhibited by machines, with machines mimicking function typically associated with
human cognition. Artificial intelligence functions include all aspects of perception, learning, knowledge
representation, reasoning, planning and decision making. The ability of these function to adapt to new
context i.e., situations that an artificial intelligence system was not previously trained to deal with, is one
aspect that differentiates strong artificial intelligence from weak artificial intelligence.
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