to study the impact of artificial intelligence upon the

To study the impact of Artificial Intelligence upon the

dynamics of Contemporary Human Resource Management

in 2020.

Sneha Dutta

Lecturer ,

Department of Business Administration

Asutosh College

Kolkata , India

[email protected]

Abstract : The workplace characteristics of 2020 have not just changed the nomenclature of

Human Resource Management to Human Capital Management , treating employees much as

capital assets yielding high ROI but also has modified the Data Base Management System

pertaining to the same. Artificial Intelligence is a technology that allows computers to learn

from or make or recommend actions based on previously collected data. Taking into

consideration the domain of HRM , AI can be applied in multiple ways to streamline the

processes and improve efficiency. The present research aims to find out the feasibility and

outcomes of an efficient implementation and inclusion of AI systems at workplace. Data was

collected from HR professionals via snowball sampling method and a simple percentage

analysis was done on the responses received from a semi-structured questionnaire. Results

revealed , despite multiple contradictions in its implementation and usage , the faith upon AI

on making SHRM interventions more sound and robust persisted in the minds of majority of

the respondents. Thus , the present research lays down a basic foundation regarding the

acceptance and feasibility of AI in the contemporary work environment of 2020.

Keywords : Artificial Intelligence , Human Resource Management , Strategic Human

Resource Management , Human Capital Management , Workforce Diversity , Employee

Centricity , Talent Acquisition , Onboarding , Training & Development , Human Resource

Audit.

Journal of Interdisciplinary Cycle Research

Volume XII, Issue V, May/2020

ISSN NO: 0022-1945

Page No:1339

Introduction : In computer science, artificial intelligence (AI), sometimes called machine

intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence

displayed by humans and animals. Leading AI textbooks define the field as the study of

"intelligent agents": any device that perceives its environment and takes actions that maximize

its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is

often used to describe machines (or computers) that mimic "cognitive" functions that humans

associate with the human mind, such as "learning" and "problem solving".

John McCarthy says that “Artificial Intelligence is the science and engineering of making

intelligent machines, especially intelligent computer programs”.

Artificial Intelligence (AI) is intelligence exhibited by machines. In computer science the

field of AI defines itself as the study of “intelligent agents”. Generally, the term “AI” is used

when a machine simulate functions that human’s associate with other human minds such as

learning and problem solving.

As machines become increasingly capable, tasks considered to require "intelligence" are often

removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler's

Theorem says "AI is whatever hasn't been done yet. For instance, optical character recognition

is frequently excluded from things considered to be AI,having become a routine

technology.Modern machine capabilities generally classified as AI include successfully

understanding human speech,competing at the highest level in strategic game systems (such as

chess and Go),autonomously operating cars, intelligent routing in content delivery networks,

and military simulations.

According to Leon C. Megginson “From the national point of view human resources are

knowledge, skills, creative abilities, talents, and attitudes obtained in the population; whereas

from the view-point of the individual enterprise, they represent the total of the inherent abilities,

acquired knowledge and skills as exemplified in the talents and aptitude of its employees”.

According to Flippo “Personnel management, or say, human resource management is the

planning, organising, directing and controlling of the procurement development compensation

integration, 4intenance, and separation of human resources to the end that individual,

organisational and social objectives are accomplished”.



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The National Institute of Personnel Management (NIPM) of India has defined human

resource/personnel management as “that part of management which is concerned with people

at work and with their relationship within an enterprise. Its aim is to bring together and develop

into an effective organisation of the men and women who make up an enterprise and having

regard for the well-being of the individuals and of working groups, to enable them to make

their best contribution to its success”.

According to Decenzo and Robbins “HRM is concerned with the people dimension in

management. Since every organisation is made up of people, acquiring their services,

developing their skills, motivating them to higher levels of performance and ensuring that they

continue to maintain their commitment to the organisation are essential to achieving

organisational objectives. This is true, regardless of the type of organisation-government,

business, education, health, recreation, or social action”.

Artificial intelligence was founded as an academic discipline in 1955, and in the years since

has experienced several waves of optimism, followed by disappointment and the loss of

funding (known as an "AI winter"),followed by new approaches, success and renewed funding.

For most of its history, AI research has been divided into sub-fields that often fail to

communicate with each other. These sub-fields are based on technical considerations, such as

particular goals (e.g. "robotics" or "machine learning"),the use of particular tools ("logic" or

artificial neural networks), or deep philosophical differences. Sub-fields have also been based

on social factors (particular institutions or the work of particular researchers).

The traditional problems (or goals) of AI research include reasoning, knowledge

representation, planning, learning, natural language processing, perception and the ability to

move and manipulate objects. General intelligence is among the field's long-term goals.

Approaches include statistical methods, computational intelligence, and traditional symbolic

AI. Many tools are used in AI, including versions of search and mathematical optimization,

artificial neural networks, and methods based on statistics, probability and economics. The AI

field draws upon computer science, information engineering, mathematics, psychology,

linguistics, philosophy, and many other fields.

The field was founded on the assumption that human intelligence "can be so precisely described

that a machine can be made to simulate it". This raises philosophical arguments about the nature

of the mind and the ethics of creating artificial beings endowed with human-like intelligence.



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These issues have been explored by myth, fiction and philosophy since antiquity. Some people

also consider AI to be a danger to humanity if it progresses unabated. Others believe that AI,

unlike previous technological revolutions, will create a risk of mass unemployment.

In the twenty-first century, AI techniques have experienced a resurgence following concurrent

advances in computer power, large amounts of data, and theoretical understanding; and AI

techniques have become an essential part of the technology industry, helping to solve many

challenging problems in computer science, software engineering and operations research.

Dynamics of Contemporary Human Resource Interventions in 2020

Human Resources (HR) have been experiencing significant changes thanks to the evolution of

information technologies in the last two decades. Today, Artificial Intelligence (AI) is

reshaping the way that companies manage their workforce and make HR plans, which increases

productivity and employee engagement in general.

Bearing in mind that employee engagement programs boost company revenue by 26%, it is

clear that one should accept AI solutions to strengthen your team and gain some long-term

benefits.

a) Employee experience : drive a company forward; therefore, it is essential to prioritize

employee experience. The focus of most HR teams has shifted towards enhancing the

experience of employees by promoting positive company culture and performance

management.

b) HR departments now focus on providing employees with a better journey map, along

with giving them exposure to feedback tools, productivity tools, advanced

communication tools, and employee wellness applications.

c) Apprenticeship : Finding skilled candidates, training low-skilled employees, and

nurturing the existing employees are some of the primary obstacles HR teams

encounter. Apprenticeship programs enables one to hire skilled talent for regulated

minimum wages and train them according to your business requirements.

d) Skill Development : Regardless of the designation or experience, constant learning is

essential for growth – both personal and professional. Therefore, HR teams need to

continually organize learning management sessions and apprenticeship programs to

make sure the skill set of all the employees is up-to-date. Employees with an improved



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skillset deliver better at the workplace, which adds to the overall performance and

productivity of the organization.

e) Online skill assessments : Assessing skills online is a relatively new trend in HR, and

companies are using it as an effective talent management tool. Online skill assessment

can come in the form of surveys, tests, quizzes, and exams. Skill assessment enables

companies to know the talent pool they have and what skills their workforce lack. After

identifying the skills the employees lack, HR teams can plan learning programs to fill

the skill gap.

f) Enhanced employee engagement : Employee engagement and employee experience

go hand in hand but have a few subtle differences. While in the past, organizations

focused on employee engagement, the trend today is to create an overall positive

employee experience that spans every single moment the employee spends with the

company.

g) Hiring Talented Employees: Hiring talented employees is a tough task, which requires

a substantial amount of time, money, and resources. And even after that, there is no

guarantee that a company will get what it is looking for. Apprenticeships can eliminate

the requirements of this tedious process at the bottom of the hiring pyramid. In

apprenticeship programs, you can hire trained young employees at minimum wages,

and give them a ‘Earn while you learn’ environment.

h) Workforce Training and Skill Development: Workforce training is essential for an

organization to grow and improve its operational productivity. However, outsourcing

training programs can be expensive, along with it being less effective. Apprenticeship

programs, on the other hand, enable you to carry out workforce training the way you

want to, without spending too much on external training sources.

i) Workforce Diversity: The primary cause of a less diverse workforce is the lack of

acquaintance and friendships. When employees train together, they get to know each

other and learn to work in teams. Improved team coordination is essential for sustaining

an agile environment in an organization, which is critical for prolonged business

growth.



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Timeline pertaining to the evolution of Artificial Intelligence :

During 20th century a brief history of AI can be given as:

1923 – Karel Kapek’s play named “Rossum’s University Robots (RUR)” opens in

London, first use of the word “robot” in English.

1945 – Isaac Asimov, alumni at Columbia University, invented the term Robotics.

1950 –Turing Test for evaluation of intelligence was introduced by Alan Turing.

Claude Shannon published detailed Analysis of chess playing as a search.

1956 – John McCarthy coined the term Artificial Intelligence.

1958 – John McCarthy invents LISP programming language for AI.

1964 – Danny Bobrow’s thesis at MIT showed that computers can understand natural

language well enough to solve algebra word problems correctly.

1979 – The First Computer controlled autonomous vehicle, Stanford Cart was built.

1984 – Dennett discusses the frame problem and how it relates to the difficulties arising

from attempting to give robots common sense.

1990 – Major advances in all area of AI:

a) Significant demonstrations in Machine Learning

b) Case-based reasoning

c) Multi-agent planning

d) Scheduling

e) Data mining, web crawler

f) Natural Language understanding and translation

g) Vision, virtual reality

h) Games

1997 – The Deep Blue Chess Program beats the World Chess Champion, Gerry

Kasparov

2000 – Interactive Robot Pets become commercially available. MIT displays a robot

with a face name – Kismet that expresses emotions.



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Literature Review : Today’s mainstream view is that neither robots nor computers will be

able to undertake the strategic and complex tasks done by HR business partners or experts,

suggesting that HR’s contribution in this area will be unaffected and could even be improved

by some of the new information, analysis and tools available. This is because machines are

good for analytical tasks but not ‘elastic thinking’. ‘If you want to create a general problem-

solving brain […] the best way is still to find a mate and create a new human being’ (Poole,

2018).

The argument could be made – as it was with the HR transformation of the early part of this

century, combining standardisation, automation and consolidation – that time and resources

will be saved by eliminating the ‘grunge’ work, thereby releasing HR to concentrate on high

value-added work. This may not have happened to the extent expected because not only has

the IT revolution been piecemeal, as noted above, but there has also been managerial

resistance to HR ‘devolving’ people management tasks to them and a lack of skills within the

HR function to take up the strategic baton (see Reilly and Williams, 2006).

Nonetheless, it could be argued, and is being argued by some HR leaders (eg Wood, 2017),

that AI offers a real opportunity for HR to make its mark.

One vital strategic task that HR should undertake is to prepare the organisation for the AI

revolution. This will mean ensuring that the workforce is ‘change-ready’ and prepared to

embrace new technology. It means thinking through organisational structures and the role of

managers so that knowledge is effectively dispersed around the business: there will be no place

for silos and turf wars.

Leadership will be distributed. Power will flow in different ways to the company organigram.

The ‘learning organisation’ may be a nearly 40-year-old term, but it could do with being

reinvented as the requirement to create a community of open minds becomes a necessity. A

culture of enquiry and innovation will be developed. In this context, and in a changed

environment of customer demand and service delivery, who is to be hired, and how they will

be developed, will have to be adjusted. Talent management may become even more important

but executed in a different way. How to keep the employees who do not sit in IT development

engaged will be a challenge in itself (Schwab, 2016).

The increasing use of AI is not without risks and not only has HR been mindful of them, but

these concerns may limit the speed of adoption. As John Hawksworth, PwC’s chief economist,

pointed out ‘legal and regulatory hurdles, organisational inertia and legacy systems will slow



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down the shift towards AI and robotics even where this becomes technically and economically

feasible’ (PwC, 2018b). Indeed, ‘the appetite of HR leaders for more digital tools may outpace

their ability to absorb the tools’ (Heric, 2018).

Furthermore, one of the impediments to this ‘absorption’ does, of course, lie in HR’s domain:

the shortage of AI skills and the difficulty of hiring sufficient talent.

Methodology

Sampling Method : Snowball Sampling

Sample Size : 40

Inclusion Criteria :

HR professionals having at least 3 years experience in Payrolling , Training & Development

, Employee Engagement & Retention.

Exclusion Criteria :

Anybody having an experience of less than 3 years in the specified domains.

Tool for Data Collection :

Semi – Structured Questionnaire drafted by extensive literature review.

Data Analysis :

Simple Percentage Analysis

Data Representation :

Bar Charts.

Data Table :

Serial

No.

Item Strongly

Agree

Agree Undecided Disagree Strongly

Disagree

1 AI will result in better

recruitment practices

25 60 12 2 1


predictive value of the

selection procedures.

45 30 5 15 5

3 AI will find its significance

while doing psychometric

55 38 3 3 1



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Page No:1346

assessment of the job

applicants.

4 AI will ensure a smooth

onboarding of the hired

incumbents.

55 28 10 5 2

5 AI will benefit SHRM

interventions by making it

more accurate yet

customized.

62 27 3 6 2

6 AI will help in

understanding what

interventions will make

sense for which individuals

, and how to improve the

Training Credibility Score

of each training session

58 14 19 6 3

7 Implementation of AI shall

foster the process of

Performance Appraisal.

69 13 7 9 2

8 AI will help the

organization by way of

more productive Individual

Development Plans for each

employee.

86 9 3 2 0

9 AI will lay a better ground

for Job Enrichment &

Enlargement plans.

79 11 8 1 1

10 AI would facilitate better

Job Analysis.

68 13 8 7 4

11 AI will help understand

which benefits matter most

to the employees.

55 19 16 8 2



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12 AI will help an organization

to predict who is likely to

leave and help manage the

level of retention.

67 19 9 4 1


management of

Compensation and Benefits.

65 19 7 6 3


maintenance of Human

Resource Information

System.

91 8 1 0 0

15 AI will make employee

interventions more

employee centric.

76 14 8 1 1

Interpretation :

Taking into consideration Item 1 , it has been noted that 60% of the respondents finds AI to be

beneficial for recruitment purposes. Item no. 2 reveals that 45% of the respondents believe AI

to raise the predictive value of the selection procedures. Item no. 3 reveals that 55% of the

respondents feel that AI helps in better psychometric assessment of the job aspirants. Item no.4

reveals that AI facilitates in a smooth onboarding of the incumbents. Item no.5 states 62% of

the respondents have strongly agreed that AI will make Strategic Human Resource

Management activities way more accurate and customized. Item no.6 states that around 58%

of the respondents have strongly agreed to the fact that AI will help in better training needs

assessments and will increase the training credibility score of each of the training sessions.

Item no.7 reveals that around 69% of the respondents believe that AI will facilitate the process

of performance appraisal. Item no.8 reveals that 86% of the respondents believe that AI will

facilitate in drafting better Individual Development Plans in an organization. Item no.9 states

that 79% of the respondents believe that AI will help in Job Enrichment & Enlargement. Item

no.10 states that 68% of the respondents strongly agree that AI will foster the process of Job

Analysis in an organization. Item no.11 states that 68% of the respondents strongly agree that

AI will help in the better understanding of compensation packages that can be offered to an

employee. Item no.12 states that 67% of the respondents feel that AI will facilitate in the



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process of employee retention. Item no.13 reveals that 65% of the respondents in total believes

that AI will be beneficial for compensation and benefits for a corporate. Item no.14 states that

91% of the respondents believe that AI will certainly enable in better maintenance of DBMS

and HRIS. Item no.15 reveals 76% of the respondents stated that in totality AI will make HR

initiatives way more employee centric.

Data Representation :

0

20

40

60

1 2 3 4 5

AI will result in better recruitment practices

0

50

1 2 3 4 5

AI will result in better predictive value of the selection procedures.

0

100

1 2 3 4 5

AI will find its significance while doing psychometric

assessment of the job applicants.

0

50

100

1 2 3 4 5

AI will ensure a smooth onboarding of the hired

incumbents.

0

100

1 2 3 4 5

AI will benefit SHRM interventions by making it

more accurate yet customized.

0

100

1 2 3 4 5

AI will help in understanding what interventions will make sense for which individuals ,

and how to improve the …



ISSN NO: 0022-1945

Page No:1349

0

100

1 2 3 4 5

Implementation of AI shall foster the process of

Performance Appraisal.

0

100

1 2 3 4 5

AI will help the organization by way of more productive

Individual Development Plans for each employee.

0

100

1 2 3 4 5

AI will lay a better ground for Job Enrichment & Enlargement plans.

0

50

100

1 2 3 4 5

AI would facilitate better Job Analysis.

0

50

100

1 2 3 4 5

AI will help understand which benefits matter most

to the employees.

0

100

1 2 3 4 5

AI will help an organization to predict who is likely to leave and help manage the level of

retention.

0

100

1 2 3 4 5

AI will result in better management of

Compensation and Benefits.

0

100

1 2 3 4 5

AI will result in better maintenance of Human

Resource Information System.



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Page No:1350

Discussion :

AI has the power to take your HR experience to the higher level. Just like Donald Southern, an

HR specialist at Resumes Planet, explained: “AI can help one to handle recruiting, productivity,

and retention more efficiently than traditional HR methods. At the same time, it also allows

you to do it faster than ever before.”

AI development in relation to job clusters within HR :

a) Administrative roles to be found in payroll and records undertaking

transactional tasks, such as data processing.

b) Posts where there is operational HR support to managers (and sometimes

employees) handling casework, recruitment, training etc.

c) Policymaking and advice as executed in centres of expertise.

d) Activities performed by business partners to strategically influence and shape

the business from a people perspective.

Talent Acquisition

Using AI, you can remove tons of stressful and monotonous work from your HR managers.

Namely, talent acquisition software can scan, read, and evaluate applicants and quickly

eliminate 75% of them from the recruiting process.

This is a huge benefit as it allows the recruiter to spend more time analyzing and evaluating

only a smaller group of eligible candidates. In such circumstances, HR units are drastically

increasing the quality of hiring decisions. Additionally, companies save a lot of money this

way because they don’t have to pay the cost of poor hiring decisions.

0

50

100

1 2 3 4 5

AI will make employee interventions more employee

centric.



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Onboarding

Hiring the most promising talents is not the only concern of HR departments. Adaptation is the

second step in the process as many prospects can’t fit in the new environment due to lack of

onboarding procedures. Namely, new employees demand a lot of attention and it is often

impossible to dedicate enough time to each one of them.

That’s where AI steps in – it determines customized onboarding procedures for every single

position. This proved to be extremely productive in practice since new workers who went

through well-planned onboarding programs had much higher retention rates than their peers

who didn’t have the same opportunity.

Training

With so many technological changes happening almost on a monthly basis, it is crucial for all

employees to keep learning and improving professional skills. AI can successfully plan,

organize, and coordinate training programs for all staff members.

Online courses and digital classrooms are the most common solutions in that regard. But this

is not the only job of AI because it also determines the best timeframe for new courses and

schedules lessons so as to fit the preferences of all employees individually.

Performance analysis

Engagement and productivity are essential qualities of successful professionals. However, most

companies are struggling to find individuals who have those traits. That’s why it is easier to

monitor their behavior and analyze key performance indicators.

Using AI tools, HR managers are enabled to set concrete objectives and let all units work in

smaller increments. This type of work is easier to follow and assess and it generates better

overall results. Of course, it doesn’t only serve to improve productivity but also to detect team

members who show lack of engagement continuously.

Retention

As much as it is difficult to hire talented employees, it is as difficult to keep them in your team.

This is why almost 60% of organizations consider employee retention their biggest problem.

However, AI has the ability to analyze and predict the needs of staff members.



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It can determine individual affinities and reveal who should get a raise or who might be

dissatisfied with the life-work balance. Such analysis gives room to HR professionals to be

proactive and solve the problem even before it actually occurs.

Today’s mainstream view is that neither robots nor computers will be able to undertake the

strategic and complex tasks done by HR business partners or experts, suggesting that HR’s

contribution in this area will be unaffected and could even be improved by some of the new

information, analysis and tools available. This is because machines are good for analytical tasks

but not ‘elastic thinking’. ‘If you want to create a general problem-solving brain […] the best

way is still to find a mate and create a new human being’ (Poole, 2018).

The argument could be made – as it was with the HR transformation of the early part of this

century, combining standardisation, automation and consolidation – that time and resources

will be saved by eliminating the ‘grunge’ work, thereby releasing HR to concentrate on high

value-added work. This may not have happened to the extent expected because not only has

the IT revolution been piecemeal, as noted above, but there has also been managerial

resistance to HR ‘devolving’ people management tasks to them and a lack of skills within the

HR function to take up the strategic baton (see Reilly and Williams, 2006).

Nonetheless, it could be argued, and is being argued by some HR leaders (eg Wood, 2017),

that AI offers a real opportunity for HR to make its mark.

One vital strategic task that HR should undertake is to prepare the organisation for the AI

revolution. This will mean ensuring that the workforce is ‘change-ready’ and prepared to

embrace new technology. It means thinking through organisational structures and the role of

managers so that knowledge is effectively dispersed around the business: there will be no place

for silos and turf wars.

Leadership will be distributed. Power will flow in different ways to the company organigram.

The ‘learning organisation’ may be a nearly 40-year-old term, but it could do with being

reinvented as the requirement to create a community of open minds becomes a necessity. A

culture of enquiry and innovation will be developed. In this context, and in a changed

environment of customer demand and service delivery, who is to be hired, and how they will

be developed, will have to be adjusted. Talent management may become even more important

but executed in a different way. How to keep the employees who do not sit in IT development

engaged will be a challenge in itself (Schwab, 2016).



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Page No:1353

HR can also contribute to working out where AI might replace humans. The function can

compare the cost of a human with the price of, say, a robot with all the ancillary training of

both employees still employed, and of the robot – not to mention the costs of displacing human

effort. At a more abstract level, HR can provide insight on what sort of relationship there might

be between humans and AI; how best to exploit highly-intelligent machines but in ways that

benefit, rather than hinder, human progress. The need for such moral oversight is explained

below.

HR should also be at the forefront of handling the consequences of organisational reskilling.

Will it simply be a matter of redundancy for those with outmoded skills and the hiring of new

people? This seems to be too simplistic, but as in previous restructuring questions, will need to

be asked not just about whether new skills can be learned by existing employees, but also

whether employees will be prepared to learn, attitudinally. Ironically, AI tools may be available

to identify those that will fit best in the newly-created roles.

More Technically the implementation of AI in worksystems will facilitate the following :

Here are some ways listed below in which AI is going to be helpful to us in the near Future -

a. Cyborg Technology: - Being human has its own flaws and one of the biggest disadvantages

of being a human is simply our own body and brain. Now, according to a researcher Shimon

Whiteson it is possible to augment ourselves with computers in the near future in order to

improve our own natural abilities. Yoky Matsuka of Nest believes that in the near future an AI

system will be developed which is going to be useful for the people with amputated limbs, as

the brain will be able to communicate with a robotic limb to provide more control to the patient.

b. Attaining Dangerous Jobs: - Robots have already begun to attain some of the most dangerous

jobs like defusing a bomb. Well, technically they are not robots; they are drones, which are

being used as the physical counterpart in bomb defusing, which requires a human to control

them, instead of using an AI system. Despite of whatever their classification is, they have saved

thousands of lives by taking over these kinds of jobs in the world. There are also some other

jobs which are being reconsidered for robot integration for example, Welding, which is quite

known for releasing earsplitting noise, intense heat and toxic substances, now can be

outsourced to robots.



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c.Virtual Personal Assistants: - Siri in iOS, Cortana in Windows 10 and even Google now have

been developed as intelligent digital personal assistants. Briefly, they help us to acquire the

useful information with voice recognition given from the user. Artificial Intelligence plays an

important role in these kinds of applications, as they collect information based on our requests

and use that information to give us the results marked up to our preferences. Microsoft has

stated that ‘Cortana tends to progressively learns about its users’ and then it will eventually

develop the ability to forecast or assume its users needs. These Virtual Personal Assistants

processes a large amount of data from different sources to learn about its users and be more

helpful to organize their daily routine.

d.Natural Language Processing: - Human Language and conversation is complex and

subjective. The current standard forms of communication with machines involve mouse and

keyboards, or a specific and basic set of verbal commands. This is different from how human

interact, simply because the amount of variability in human communication; ‘red’ in ‘red hair’

is different from ‘red’ in ‘red apple’. This fundamental problem of correctly representing

concepts with symbols, or words, is greatly hindering the progression of Natural Language

Processing. If these challenges are overcome, systems with Natural Language Processing

would have the capabilities to express beliefs they have acquired, translate languages at human

translator levels, understand the difference between a red apple and red hair, and process

commands like ‘hand me that purple thing down there’ into physical action.

e. Detection: - Most of the banks send those types of mails just to confirm whether there’s

been a fraud committed from your account or not and in order to confirm that you had accepted

the purchase before giving the money over to some other company. The technology that’s been

deployed to monitor for this type of fraud is Artificial Intelligence. Computers deal with a huge

amount of deceitful and non-deceitful purchases and asked to learn to look for signs that a

transaction falls into one category or another. After training this kind of AI well enough it will

be able to identify a fraudulent transaction based on the signs and indications that it learned

through the training exercise.

f. Security Surveillance: - We as humans are not really good as multi – tasking because our

brain will start to mess things up and monitoring a large number of security cameras being a

single person isn’t a very secure system; and people tends to easily get bored, and even in the

best of circumstances keeping track of multiple monitors at a time can be quite difficult.



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g. Human – Machine Interaction: - Most technology literate people, today, are accustomed to

the idea that interacting with a computer is just different then interacting with a human. There

is a push towards human – centered interfaces which emphasize removing the mechanical

feeling inputs from machines and making them more humanlike. This requires video input to

track facial features and emotional cues, video input to track human movements and recognize

actions, audio input that can detect emotions and different types of commands, audio input that

can hear and process natural language. Detecting emotion allows for machines to behave in a

more anthropomorphic manner because humans will recognize emotion and adjust the

Interaction accordingly. By analyzing facial expressions, body language, conversations tones,

and actual dialog, systems can anticipate human needs. This would also be useful in emotional

development research; tutoring and mental disorders just to name a few. Machines are already

being developed for a wide range of autonomous tasks.one of these machines would be used

as soldiers capable of lethal force, or as machines that can physically assist the elderly or

infants. Machines that are given the capacity to use lethal force or care for those that need help

have a high possibility of making life changing decisions because of a lack of understanding.

It is crucial for machines like these to understand the full picture and not only respond to simple

but basic verbal commands.

Certain contradictions of the implementation of AI at workplace may be analysed in the

following manner :

There are abundant complications when trying to create an intelligent system. Much of the old

or simple AI is a list of conditions for what reaction to have based on expected stimuli. But this

is arguably not intelligence, and imitating true intelligence requires an understanding of how

the input relates to the output, as well as large interdisciplinary effort among most AI subfields

along with psychology and linguistics.

Many complications involve ‘Human – Machine interaction’ because of the complexity of

human interaction. A lot of the communication that happens that happens between humans

cannot be coded facts a machine could simply recite. There are hundreds of subtle ways that

humans interact with each other that affect communication. Innovation in voices, body

language, and response to various stimuli, emotions, popular culture facts, and slang all affect

how two people might communicate. This is hard to model in a machine that does not have

basic common sense model already in place that can learn or make inferences.



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‘Fuzzy logic’, which is modeled after humans’ excellent ability of making approximations

without any real values, poses many complications. Computation by definition, require

numbers and not words or concepts.

Complications arise while trying to imitate human intuition or common sense. The amount of

background information that is taken for granted by humans is immense and hard to replicate

in machine.

There is difficulty in trying to imitate human emotion because of how complex and subjective

they can be, especially when multiple emotions are expressed.

When using a Machine Learning approach, the system will process conversations that have

been labeled by humans, but these labels are not always consistent.

‘Image Processing’ also has complications with recognizing different locations from photos on

the internet because of the variability in images.

Modeling the world from internet photos is difficult because of how much the average internet

photos varies.

Generally, image processing requires data to be somehow consistent, but that obstacle will have

to be overcome to render 3D models of popularly photographs locations on Earth. Simply

detecting what an image contains is a tricky process.

Conclusion

AI is everywhere these days – from simple calculators to flight controls and space operations.

It also enables HR executives to improve results and monitor employees more efficiently.

References :

1.“A Literature Review on Artificial Intelligence”, Volume 19, Number 4, pp. 535-570, 2008, S.A. Oke,

University of Lagos, Nigeria.

2.“A Review of Artificial Intelligence”,2009, E.S. Brunette,

R.C. Flemmer and C.L. Flemmer, School of Engineering and Advanced Technology, Massey

University, Palmerston North, New Zealand.

3.“A Literature of Artificial Intelligence”, Sam Olds, WRTG 3014,April 24,2014



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4. Daniel Merkle; Martin Middendorf (2013). "Swarm Intelligence". In Burke, Edmund K.; Kendall,

Graham (eds.). Search Methodologies: Introductory Tutorials in Optimization and Decision Support

Techniques. Springer Science & Business Media. ISBN 978-1-4614-6940-7.

5. "What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual

binary logic?". Scientific American. Retrieved 5 May 2018.

6. Bayesian inference algorithm:

Russell & Norvig 2003, pp. 504–519,

Poole, Mackworth & Goebel 1998, pp. 361–381,

Luger & Stubblefield 2004, pp. ~363–379,

Nilsson 1998, chpt. 19.4 & 7

7. McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of

Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of

Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73,

"[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with

"cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having

either to accept Norbert (not Robert) Wiener as a guru or having to argue with him."

8. Griewank, Andreas (2012). Who Invented the Reverse Mode of Differentiation?. Optimization

Stories, Documenta Matematica, Extra Volume ISMP (2012), 389–400.

9. Paul Werbos, "Beyond Regression: New Tools for Prediction and Analysis in the Behavioral

Sciences", PhD thesis, Harvard University, 1974.

10. Paul Werbos (1982). Applications of advances in nonlinear sensitivity analysis. In System modeling

and optimization (pp. 762–770). Springer Berlin Heidelberg. Online Archived 14 April 2016 at the

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to study the impact of artificial intelligence upon the

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