In the global economy, the Artificial Intelligence is becoming a prominent factor, which was screened in the previous post of this article. In the history, Artificial Intelligence commonly known as AI came into existence in 1956 (Nasser, Anis Moosa and Ananda, 2021). John McCarthy coined the term ‘AI’ in 1956 and defined it as ‘the science and engineering of making intelligent machine’. Still, in modern times, AI is the science and art of making intelligent agents or machines (Ashwani, Kamal and Jayanthi, 2021, p.8). From this statement, definition of AI can be understood in a clear perspective in the present context, though definitions of AI have also changed in the course of time. “Imitating intelligence human behavior”, gives a much stronger definition (Joost, Egbert, Walter, Peter and Mannes, 2009). In addition, the artificial intelligence community has been trying to imitate intelligence behavior with computer programs. If the computer wants to be called intelligent, it must be able to do many different things (Joost, Egbert, Walter, Peter and Mannes, 2009), because it has to be equal to human intelligence or above.
AI which is a specialized area of information Technology, focuses on the simulation of human intelligence process by machines has become an important area in the ongoing global fourth industrial revolution (Aparrajitha, 2019, p.164). In fact, the Artificial Intelligence is not the mere imitation of human intelligence. It also servers to carry out activities that people have not been able to carry out so far, not so quickly and/ or not so well (Ralf and Marie, 2020, p.24). So, the task or process of AI is to help to carry out the work which is beyond the capacity of human too.
The overall task of AI work is based on the decision-making process which differs from decision-making processes of a human. The following figure clearly describes it.
Source: Stefan H.Vieweg, 2021, Human-Machine interaction
The above figure helps to understand these use of AI supports in the sense of information processing for evidence-based decisions and to avoid cognitive (human) bias, on the other hand skills, beliefs, and values of individuals which are crucial to find the ‘right’ decision in a changing context (Stefan, 2021). Here the decision-making process of AI, always focuses on the right decision in any context. Therefore, the use of AI can perfect the task which makes to go beyond the human task.
Artificial
Intelligence in itself has two common subdivisions. They are Weak AI, which has
the aim to achieve human abilities on the same or a slightly higher level and
Strong AI, which describes the endeavor to achieve human capabilities through
the use of technology in almost all areas of our everyday lives (Ralf and
Marie, 2020). So, the depending on the need or task, AI becomes the standard of
functions.
According to Stefan Strohmeier (2022), in human resource, AI has categorized by strength, paradigm, convention and function and they are follows,
Categorization
by Strength:
1.
Narrow AI (also known as week AI) –
aims at tacking a more or less defined, single human task
2.
General AI (also known as strong AI,
artificial general intelligence [AGI], full AI or human-level AI [HLAI]) - aims
at tacking any task human can perform based on NI (Natural Intelligence)
3. Super AI (also known as artificial super intelligence) – aims at qualitatively and quantitatively outperforming humans in any task and beyond, allowing the performance of tasks that cannot be completed by humans due to their NI restrictions.
Categorization
by paradigm:
1.
Symbolic AI (also known as good
old-fashioned AI [GOFAI]) – is a paradigm within which humans build a model of
reality by using symbolic representations such as words or phrases and AI
employs or manipulates this symbolic model to achieve results.
2.
Connectionist AI (also known as
sub-symbolic AI or non-symbolic AI) – is a paradigm that uses representations
of reality such as pixels or structured data to let AI itself learn a model of
reality by connecting known inputs and outputs.
Both AI paradigms are discussed and applied in HR.
Categorization
by convention:
Conventions
are historically emerged agreeing on categorizing AI, as for instance
manifested in AI textbooks, journals, conference, and departments.
Conventional AI fields are rather distinct, are based on different foundations and use different methods to solve different problems.
Categorization
by function:
Functions
designate the categories of NI that AI intends to mimic.
(Stefan, 2022,
pp.2,4)
The above categorizations give the overall understanding about AI in Human Resource which is merely noted in the present context of business scenario.
Functions of AI
The functions of AI are defined by the application and system of AI. The most popular AI applications are mostly automation and robotics (36%) for organized operational functions; robotic helpers or interaction framework (32%) for client service sections; and machine-learning techniques (25%) (Nasser, Anis Moosa and Ananda, 2021, p.23)
New hires can be trained for telecommuting so that faster integration, learning of leave rules and remote work policies could be enhanced. AI-based chatbots would facilitate these processes, empowering new hires to be integrated into the workforce (Ashwani, Kamal and Jayanthi, 2021).
While it is difficult to find coaches due to geographical and time constraints. AI assisted virtual coaches can be urged online and virtually to replace face to face interaction and engage clients via assisted learning scenario (Ashwani, Kamal and Jayanthi, 2021).
"AI- based PM (Performance Management) systems are helping supervisors to identify areas for performance improvement. These solutions offer capabilities for developing personalized, measurable and engaging programs to help employees achieve their personal and professional goals" (Ashwani, Kamal and Jayanthi, 2021, p.17).
These functions
of AI show the capabilities of which go beyond the human limitations.
References:
Aparrajitha Ariyadasa (2019), ‘A Challenge from Humanoid Bots: An Analysis of Legal Regime in Sri Lanka on Artificial Intelligence’, International Conference On Business Innovation (ICOBI) 22 November, p.164, Colombo, Sri Lanka
Ashwani Upadhayay, Kamal Khandelwal and Jayanthi Iyengar 2021, AI Revolution in HRM, SAGE, India and England
Joost N. Kok, Egbert J.W.Boers, Walter A. Kosters, Peter van der Putten 2009, Artificial Intelligence: Definitions, Trends, Techniques and Cases, Netherland
Nasser Rashad Al Mwali, Anis Moosa Al Lawati and Ananda S (edts) 2021, Fourth Industrial Revolution and Business Dynamics: Issues and Implications, Palgrave Macmillan, Singapore
Ralf T. Kreutzer, Marrie Sirrenberg 2020, Understanding Artificial Intelligence: Fundamentals, Use cases and Methods for a Corpprate AI Journey, Spinger Nature, Switzerland
Stefan H. Vieweg (edt) 2021, AI for the Good: Artificial Intelligence and Ethics, Springer Nature, Switzerland
Stefan Strohmeier (edt) 2022, Handbook of Research on Artificial Intelligence in Human Resource Management, Elgar, UK and USA
