CC BY
3ОБРАЗОВАНИЕ
Human Capital Management in the Context of Artificial Intelligence
Shahnazaryan Nelli H.
Associate Professor at the faculty of Management Ph.D. in Economics Armenian State University of Economics (Yerevan, RA),
https://orcid.org/0000-0002-5409-6832 nelly.shahnazaryan@gmail.com Shahnazaryan Gagik H.
Lecturer of the chair of Business Administration of the Faculty of Marketing and Business Organization, Armenian State University of Economics (Yerevan, RA),
https://orcid.org/0000-0003-1573-7200 g.shahnazaryanl959@gmail.com
UDC: 308; EDN: ERMZQP; JEL: A2, E24, J24, H75, I2 DOI: 10.58587/18292437-2024.1-78
Keywords: Artificial Intelligence, Human Capital Management, Education, AI Subsets, New Opportunities
Управление человеческим капиталом в контексте искусственного интеллекта
Шахназарян Нелли А.
к.э.н., доцент кафедры Менеджмента факультета Управления Армянский государственный экономический университет (Ереван, РА)
Шахназарян Гагик А.
преподаватель кафедры Делового администрирования факультета Маркетинга и организации бизнеса, Армянский государственный экономический университет (Ереван, РА)
Аннотация: Искусственный интеллект (IA) выступает в качестве преобразующей силы, имеющей глубокие последствия для различных областей, включая управление человеческим капиталом (HRM), в частности в сфере образования. В образовании интеграция IA обещает революционизировать традиционные методы обучения, предлагая адаптивный опыт обучения и персонализированную обратную связь, бросая вызов традиционным педагогическим структурам. Тем не менее, риски включают растущее неравенство в доступе к качественному образованию и разрушение человеческих связей в учебной среде, что подчеркивает необходимость этичного управления IA и подходов, учитывающих социально-культурные особенности. В конечном счете, эффективное использование искусственного интеллекта в сфере образования требует тонкого понимания его возможностей и опасностей в рамках социокультурных контекстов, в которых они функционируют, способствуя достижению справедливых и инклюзивных результатов.
Ключевые слова: искусственный интеллект, управление человеческим капиталом, образование, ИИ составляющие, новые возможности
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Artificial Intelligence, frequently shortened to AI, emerges as one of the foremost captivating and transformative fields of modern science and technology. It is a fascinating field that has captured the imagination of scientists, engineers, and dreamers alike. AI encapsulates not just the technical prowess of creating intelligent machines but also delves into profound philosophical questions about the nature of intelligence, consciousness, and the human experience. In this context, Alison Cawsey, prominent researcher, and author in the field of artificial intelligence, in her seminal work, "The Essence of Artificial Intelligence" [1] (1997) artificial intelligence define as the endeavor to replicate and extend human intelligence through computational means, encompassing a spectrum of techniques and methodologies aimed at creating machines capable of perceiving, reasoning, learning, and adapting in ways that emulate human cognition. Her work encompasses a multifaceted exploration of AI, combining philosophical inquiry, ethical reflection, technical expertise, and societal involvement to enhance our comprehension of artificial intelligence and its significant ramifications for mankind. Ethical considerations are central to Cawsey's work. She critically examines the ethical implications of AI technologies, addressing issues such as bias, fairness, transparency, accountability, and the societal impact of intelligent systems. Cawsey advocates for the responsible and ethical use of AI to ensure that it aligns with human values and promotes the common good.
The Oxford Learner's Dictionaries describe the Artificial Intelligence as the study and development of computer systems that can copy intelligent human behavior [13].
So, AI refers to the simulation of human intelligence in machines that are programmed to think, learn, and problem-solve like humans. It encompasses a range of technologies and methodologies designed to empower computers or machines to execute tasks usually associated with human intelligence, like comprehending natural language, identifying patterns, decision-making, and adjusting to novel circumstances. AI systems can be broadly categorized into two types:
> Narrow AI (Weak AI):
> Artificial General Intelligence (AGI):
Narrow AI pertains to AI systems crafted and
trained to execute a particular task or series of tasks.
These systems excel in performing specific tasks within a limited domain but do not possess general intelligence or understanding. Examples of narrow AI include virtual assistants like Siri and Alexa, image recognition systems, recommendation algorithms, and autonomous vehicles.
AGI, alternatively referred to as Strong AI or Human-level AI, denotes artificial intelligence systems capable of comprehending, acquiring knowledge, and deploying intelligence across diverse tasks and fields, akin to human cognitive abilities. The realization of AGI remains conjectural and has yet to be accomplished.
AI technologies encompass various techniques and methodologies, including:
❖ Machine Learning (ML), a facet of artificial intelligence (AI), encompasses the process of instructing algorithms to glean insights from data and make informed predictions or decisions devoid of explicit programming. Techniques like supervised learning, unsupervised learning, and reinforcement learning are frequently utilized in machine learning endeavors.
❖ Deep Learning (DL), a subset of machine learning, employs complex artificial neural networks comprising multiple layers (referred to as deep neural networks) to model and decipher intricate patterns within datasets. Notably, deep learning has demonstrated remarkable achievements in various domains such as image recognition, natural language processing, and speech recognition.
❖ Natural Language Processing (NLP) represents a segment of AI concentrated on facilitating computers to comprehend, interpret, and generate human language. NLP methodologies find utility in diverse applications like language translation, sentiment analysis, chatbots, and text summarization.
❖ Computer Vision entails the formulation of algorithms and strategies to enable computers to perceive and dissect visual data extracted from images or videos. Applications of computer vision encompass tasks such as object detection, facial recognition, medical image analysis, and autonomous driving.
❖ Robotics amalgamates AI with mechanical engineering to conceptualize and fabricate intelligent machines adept at executing tasks in the physical realm. The spectrum of robotics applications spans from industrial automation and
manufacturing to healthcare, agriculture, and even space exploration.
At its core, the essence of AI is about creating algorithms, systems, and technologies that exhibit intelligence the ability to perceive their environment, comprehend intricate patterns, render decisions, and execute actions to accomplish particular objectives. This intelligence can manifest in various forms, from simple rule-based systems to complex neural networks capable of deep learning and autonomous decision-making.
For more deep understanding of AI we need to have overview of Artificial Intelligence history. The history of AI is a captivating journey marked by visionary ideas, technological breakthroughs, and periods of both enthusiasm and skepticism. The concept of artificial beings endowed with humanlike qualities can be traced to ncient civilizations, with myths and folklore featuring tales of artificial creatures such as the ancient Greek automata. Philosophers like Aristotle contemplated the nature of thought and reason, laying the groundwork for future inquiries into artificial intelligence. The contemporary era of AI commenced in the 1950s, marked by seminal contributions from pioneers including figures like Alan Turing, who introduced the Turing Test to gauge machine intelligence, and John McCarthy, credited with popularizing the term "artificial intelligence" and orchestrating the Dartmouth Conference in 1956, considered a seminal event in the establishment of AI as an academic discipline. The 1950s and 1960s witnessed rapid progress in AI research, with initial efforts focused on symbolic AI, logic, and problem-solving. Researchers developed programs capable of playing games like chess and checkers, as well as symbolic reasoning systems such as the Logic Theorist and General Problem Solver. Despite early successes, the field of AI experienced a period of stagnation and reduced funding known as the "AI winter" in the 1970s and 1980s. Initial optimism gave way to disillusionment as early AI systems failed to live up to expectations, leading to a decline in interest and investment in AI research. During the AI winter, interest shifted towards expert systems and knowledge-based AI, which focused on encoding human expertise in rule-based systems. Expert systems found applications in fields such as medicine, finance, and engineering, demonstrating the practical potential of AI technologies. The 1990s saw a resurgence of interest in AI, driven by advancements in computing power, algorithmic techniques, and the availability of large datasets. Machine learning emerged as a dominant paradigm, with approaches such as neural networks, genetic algorithms, and support vector machines gaining traction. In the 2000s, there was a surge in deep
learning, a subset of machine learning concentrating on training deep neural networks with numerous layers. Progress in deep learning algorithms, combined with the accessibility of vast datasets and potent GPUs, transformed AI research and facilitated notable strides in domains like computer vision, natural language processing, and speech recognition. Presently, AI technologies are seamlessly integrated into various facets of our daily routines, fueling virtual assistants, recommendation systems, autonomous vehicles, and more. Ongoing research is dedicated to enhancing AI capabilities in fields like reinforcement learning, unsupervised learning, explainable AI, and artificial general intelligence (AGI), with the overarching objective of constructing intelligent systems that match or surpass human-level intelligence.
Overall, the history of AI is characterized by a dynamic interplay of scientific discovery, technological innovation, and societal influence, shaping the trajectory of AI research and its impact on the world. As AI continues to evolve, its history serves as a testament to human ingenuity and our relentless pursuit of understanding and harnessing the power of intelligence.
Yet, the essence of AI transcends mere technical achievements and delves into profound philosophical inquiries. Questions about the nature of consciousness, self-awareness, and the ethical implications of creating sentient machines challenge our understanding of what it means to be intelligent. The prospect of artificial general intelligence (AGI) — machines possessing human-level intelligence — raises existential questions regarding the destiny of humanity and our role in shaping the evolution of intelligence. Moreover, the essence of AI intersects with broader societal, ethical, and cultural dimensions. Concerns about job displacement due to automation, algorithmic bias in decision-making, privacy violations, and the concentration of power in the hands of AI developers underscore the need for responsible AI development and regulation. The essence of AI demands not just technical expertise but also ethical reflection, empathy, and a commitment to ensuring that AI serves the collective good and upholds human values.
Now, let's examine the trajectory of artificial intelligence growth. According to the most recent data available as of 2023, the global AI market stands at $196.63 billion, reflecting an increase of roughly $60 billion since 2022. This surge is primarily attributed to the expanding practical applications of AI technology, spanning from content creation to autonomous vehicles. Projections suggest that the global AI market is anticipated to reach $1.81 trillion by 2030. Although the current AI market is substantial, it is poised to expand by
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more than thirteen times over the next decade. Throughout this forecasted period, experts anticipate
the AI market to undergo a compound annual growth rate (CAGR) of 38.1% [7].
140 120 100 80 60 40 20 0
54%
126
45% 47% 94.41
70.9438% 3% 33 %
51.27 ■
34.87 ■
10.1 14.69 22.59 ■
0 ■ ■
0.6 0.5 0.4 0.3 0.2 0.1 0
2018
2025
2019 2020 2021 2022 2023 2024 Amount (Billion USD) Growth Percentage (%)
Figure 1. Global Artificial Intelligence Software Market Revenue (2018-2025) (Projected) [10]
As per Omdia, the worldwide yearly revenue from AI software presently exceeds $70 billion. Should the anticipated growth projections materialize within the AI sector, additional human resources will be essential (see Figure 1). By the year 2025, an estimated 97 million individuals will be needed to meet the escalating demands of this surging industry [5; 15].
It is also important to understand how businesses incorporate artificial intelligence. Around 4 in 5 companies consider AI a foremost concern in their business strategies. An impressive 83% of companies prioritize integrating AI into their business strategies. Automated emails and chatbots stand out as prevalent AI applications in daily business communications [6].
The ChatGPT, which is a conversational AI model developed by OpenAI, capable of generating human-like responses in natural language conversations across diverse topics and contexts, leveraging the GPT architecture trained on extensive volumes of textual data sourced from the internet, in late 2022, there was a remarkable achievement as the AI platform garnered 1 million users within a week. By early 2023, ChatGPT had amassed more than 100 million monthly users.
According to PWC around 17 in 20 CEOs anticipated AI becoming a "mainstream technology" within their company by 2021. Statistical evidence indicates that AI holds the capability to enhance employee productivity by around 40% by 2035. In light of this, certain projections propose that AI technology could yield $15.7 trillion in revenue by 2030, consequently bolstering the GDP of local economies by an extra 26% [4].
It is also noteworthy that:
❖ Projections indicate that the AI market in the United States is poised to achieve a value of $299.64 billion by 2026 [17].
❖ Netflix generates annual revenue of $1 billion through automated personalized recommendations [12].
❖ About 48% of businesses employ AI in some capacity to effectively leverage big data [14].
❖ 38% of medical providers utilize computers as an integral part of their diagnostic processes [5].
Nevertheless, 40% of executives acknowledge that the current cost of implementing advanced AI technologies and the requisite expertise is prohibitively high [16].
The rise of artificial intelligence (AI) has sparked concerns about potential unemployment and job displacement in various industries. While AI technologies offer numerous benefits, including heightened efficiency, productivity, and innovation, AI systems also possess the capability to automate tasks conventionally executed by humans, potentially causing disruptions in the workforce.
AI technologies, particularly machine learning and robotics, excel at automating routine and repetitive tasks across various industries, including manufacturing, transportation, retail, and customer service. As these tasks become automated, there is a risk of job displacement for workers who perform them, particularly those in low-skilled or routine-based occupations. Also, the transition to an AI-driven economy may exacerbate skill mismatches in the labor market, as the demand for workers with technical, analytical, and digital skills increases while the demand for low-skilled jobs decreases. This can lead to unemployment for workers who lack the necessary skills to adapt to the changing job landscape. Addressing these skill gaps through education, training, and reskilling programs is essential to mitigate unemployment and ensure a smooth transition to the AI-driven economy.
The automation of routine tasks can lead to job polarization, where high-skilled, high-paying jobs
and low-skilled, low-paying jobs increase, while middle-skilled jobs decline. This trend can exacerbate income inequality, as workers in low-skilled occupations face job insecurity and downward pressure on wages, while those in high-skilled occupations benefit from increased demand and higher salaries. In some cases, the widespread adoption of AI technologies may lead to structural unemployment, where entire industries or sectors experience prolonged job losses due to technological disruption. This can have far-reaching economic and social consequences, including
reduced consumer spending, increased social welfare costs, and social unrest.
The displacement of human workers by AI raises ethical and social implications, including questions about fairness, equity, and the distribution of wealth and resources. It also underscores the need for policies and regulations to ensure that the benefits of AI are shared equitably and that vulnerable populations are protected from the negative effects of automation.
Let's now consider what kind of jobs may face significant changes or even become obsolete.
Administrative and executive secretaries Accounting, Bookkeeping and payroll clerks Security guards
Building caretakers and housekeepers Cashier and ticket clerks Material-recording anjLstock-keeping clerks Assembly and factory workers Postal service clerks
_Bank tellers and related clerks
Shop salespersons ^^laauiteters
—Cliaaliafermation and customer service workers Business services and administration managers
Door-to-door sales workers, news and street vendors, and related workers 0 -1 -2 -3 -4 -5 -6 -7 -8 -9
Figure 2. Largest job de<
Here are some types of jobs that may be impacted:
1. Repetitive and Routine Jobs: Jobs that involve monotonous, repetitive tasks are at risk. For instance:
> Data Entry Clerks: AI can efficiently handle data entry and processing.
> Assembly Line Workers: Automation can replace manual assembly tasks.
2. Customer Service and Call Centers: Basic customer inquiries can be handled by AI-powered chatbots and virtual assistants.
3. Transport and Delivery Drivers: With the advent of self-driving vehicles, traditional driving jobs may decline.
4. Cashiers and Retail Salespeople: Automated checkout systems and online shopping reduce the need for human cashiers.
5. Manufacturing and Factory Workers: AI-
driven robots can perform tasks like welding, painting, quality control, etc.
•, millions [2; 31]
6. Telemarketers: Cold-calling and telemarketing roles may diminish as AI handles outreach.
7. Document Review and Legal Research: AI algorithms can analyze legal documents and precedents more efficiently.
8. Routine Medical Diagnostics: AI can assist in diagnosing routine medical conditions, affecting roles like radiologists.
9. Bookkeeping and Accounting: Automated software can handle financial record-keeping.
10.Warehouse Workers: AI-driven robots can manage inventory and logistics.
Nevertheless, it's important to recognize that AI is improbable to substitute roles demanding human capabilities like judgment, creativity, physical agility, and emotional intelligence (Picture 1).
Surveys conducted for the Future of Jobs Report indicate that the most substantial job expansion between 2023 and 2027 will be observed
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in occupations such as agricultural equipment vocational education instructors. Mechanics and operators, heavy truck and bus drivers, and machinery repair personnel rank fourth in this list.
Picture 1. Businesses' Top 10 Skill Priorities for 2027 [9]
Figure 3. Largest job growth, millions [2; 31]
This implies that one of the human brain's significant advantages over AI lies in its connection to an actual human body. Indeed, the anticipation of machines displacing physical and manual labor has diminished, with companies surveyed for the report revising down their projections for further automation. They now estimate that 42% of tasks could be automated by 2027, compared to predictions in the Future of Jobs Report 2020 suggesting that 47% could transition this way by 2025. Surveys for the 2023 report indicate a net increase of 2 million jobs, equivalent to 12.5% of the workforce, expected in the supply-chain and logistics sector. Trends in this domain are influenced by shortages of heavy truck and lorry drivers observed in mid-2022. However, relatively modest
expectations regarding the impact of autonomous vehicles on job creation, as depicted in the chart below, also indicate that the occupation of driver is unlikely to vanish in the foreseeable future [8].
However, in numerous instances, individuals with higher levels of education are likely to face more significant threats compared to those with lower levels of education. Consider, for instance, individuals employed in hotel room cleaning -automating such tasks proves to be extremely challenging. In essence, the report from the Forum indicates that 50% of organizations anticipate AI to foster job growth, whereas only 25% anticipate it resulting in job losses [8].
In summary, as mentioned above, the skills most sought after by employers in the next five
years will encompass analytical thinking, empathy, active listening, leadership, and social influence, as outlined in the report.
Addressing the issues of unemployment caused by artificial intelligence requires a multi-faceted approach involving policymakers, businesses, educators, and workers. This includes investing in education and training programs to equip workers with the skills needed for the jobs of the future, implementing policies to support displaced workers through job retraining and income support, and fostering a culture of lifelong learning and adaptability in the workforce. Additionally, responsible AI development and deployment practices can help mitigate the negative impact of automation on employment by prioritizing human-centric design, transparency, and accountability. By addressing these challenges proactively, society can utilize the transformative capabilities of AI while minimizing its adverse effects on employment and livelihoods.
It is noteworthy that human capital management change its essence as well. Particularly in the context of education. It is clear now that artificial intelligence most knowledge of the world and can answer most questions, when human even scientist are unable. People can specialize in limited fields, and though incomplete. It's not humanly possible. AI has no limitations. But there is one thing he cannot do, and it is much less likely that he will ever be able to do it. To make judgments and implement innovations, especially large ones. AI works within the framework that human give him and does not create a new framework for himself. In addition, it cannot improve on its own without human interaction. However, it's essential to acknowledge that artificial intelligence algorithms are engineered to make decisions, frequently utilizing real-time data. Unlike passive machines limited to mechanical or predetermined responses, AI algorithms amalgamate information from various sources, analyze it instantaneously, and act upon the insights obtained. Utilizing sensors, digital data, or remote inputs, they process data from diverse origins. Consequently, they are intentionally designed by humans and draw conclusions based on immediate analysis [3].
So, the field of artificial intelligence is developing rapidly and it's quite difficult to understand what they might look like in the near future. In this context the question arises about the availability of high-quality human capital that will have a deep understanding of artificial intelligence, provide its support, be able to use it and make improvements. Thus, this should be a challenge for modern countries. This goal can be achieved through education. Because at the university, people
get the knowledge they use and acquire skills. Of course, they can improve their knowledge through training and self-education, but the university provides the basis for knowledge. And here we have a confusion - why do we need education and science if we can just ask AI. We can, but without understanding the concept, a person will not be able to offer a single valuable innovation. Because it's one thing to know, and another to use. To use it, we need a deep learning process, not just asking questions and getting quick answers.
It is noteworthy that according to The Cambridge Handbook of Artificial Intelligence [11 ].
Advanced artificial intelligence (AI), or superintelligence, holds the potential for significant disruption across the realms of law, economy, and society. The world stands on the brink of a critical juncture, facing the existential threat posed by superintelligence, which could potentially supplant human control and decision-making. It is imperative to prioritize efforts aimed at mitigating the adverse impacts of such disruption and employing intelligent design strategies to preclude AI from ever evolving into an existential threat to humanity.
However, it is noteworthy that above-mentioned process is being guided and implemented by human. Therefore, despite the technology growth including the use of artificial intelligence the human is the key player in whole process. However, not ordinary human - the highly educated person, whom we will call talent. The robots and artificial intelligence can replace the job of not qualified human, but not talents, since machine cannot make judgement and think out of scope when talent can and will. In this context, there is a big problem to manage them - teach, guide, motivate, assess and promote. It is too sensitive, since talents are unique and it is too difficult to make a talent from the ordinary human, but too easy to lose them with the loss of whole efforts and resources invested. Therefore, the talent management is very sensitive issue and should be considered too serious. Therefore, this process must begin with education.
Considering above mentioned we suggest:
❖ In schools create new subject called "Introduction to Artificial Intelligence", where pupils will get basic understanding of artificial intelligence and neural networks.
❖ In universities to make obligatory the course of "Basics to Artificial Intelligence", where student get overall understanding about artificial intelligence and deep knowledge of one of its techniques (Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Robotics). Since there is a shortage of qualified specialists with appropriate digital competencies, and it will be difficult to hire highly
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qualified specialists for teaching, we suggest that universities conduct artificial intelligence classes online on a flexible schedule. This will enable universities to hire highly qualified specialists from outside as additional work for them.
❖ Organize the training of teachers for learning based on artificial intelligence and set up artificial intelligence systems to understand educational processes.
❖ Using international research, create a list of occupations that may become obsolete in the near future. We suggest that employees of these occupations be given the right to use the income tax amount for artificial intelligence training.
❖ Create a database of Armenian online AI courses where older people who wish can study in their native language.
Also we suggest to use Artificial Intelligence in the higher education. In the realm of higher education, AI offers immense potential to enhance learning outcomes, streamline administrative processes, and personalize the educational experience for students. Personalized learning platforms powered by AI have the capacity to address the unique needs, preferences, and learning methods of individual students. These platforms employ algorithms to scrutinize student data, including performance metrics, levels of engagement, and individual preferences, thereby facilitating tailored learning experiences for each student. For example, platforms like Khan Academy and Coursera leverage AI to recommend specific courses, modules, or learning materials based on a student's academic background, interests, and learning pace. This personalized approach not only increases student engagement but also enhances retention and comprehension of course materials. Also traditional methods of assessment often rely on standardized tests or assignments, which may not accurately gauge a student's true understanding or abilities. AI-powered adaptive assessment tools offer a more nuanced approach by dynamically adjusting the difficulty and format of questions based on a student's responses. For instance, platforms like ALEKS and Smart Sparrow use AI algorithms to analyze student responses in real-time and adapt subsequent questions to target areas of weakness or challenge. This adaptive assessment not only provides more accurate insights into student mastery but also enables educators to identify areas where additional support or intervention may be needed. AI-driven intelligent tutoring systems offer students personalized support and guidance outside the traditional classroom setting. These systems simulate one-on-one tutoring experiences by providing real-time feedback, explanations, and hints tailored to individual student needs. For
example, Carnegie Learning's Cognitive Tutor and Duolingo's language learning platform utilize AI algorithms to assess student performance, identify misconceptions, and deliver targeted interventions. These systems not only help students' master complex concepts but also promote self-directed learning and problem-solving skills. In addition to enhancing the learning experience, AI technologies can streamline administrative processes within higher education institutions. AI-powered chatbots, for example, can handle routine inquiries, such as admissions procedures, course registration, and financial aid inquiries, freeing up staff to focus on more complex tasks. Furthermore, AI analytics tools can analyze vast amounts of data, such as student demographics, academic performance, and retention rates, to identify patterns and trends that can inform strategic decision-making. For example, institutions can use predictive analytics to identify students at risk of dropping out and intervene proactively to provide support and resources. Conclusion:
Artificial Intelligence (AI) represents a paradigm shift in how we interact with technology, offering immense potential to revolutionize various aspects of society. However, whether AI is perceived as an opportunity or a danger largely depends on people's readiness to navigate the complexities of the AI landscape and meet the criteria of an AI-driven reality. In modern conditions the focus should be on reskilling and upskilling the workforce to adapt to the evolving job landscape. Human skills like critical thinking, creativity, and empathy will remain valuable even in an AI-dominated world. Also Artificial Intelligence has the potential to revolutionize higher education by personalizing learning experiences, improving assessment methods, providing intelligent tutoring support, and streamlining administrative processes. By leveraging AI technologies effectively, higher education institutions can better meet the diverse needs of students, enhance learning outcomes, and prepare graduates for success in the rapidly evolving workforce of the future. However, it is essential to approach the integration of AI in education thoughtfully, ensuring that ethical considerations, data privacy, and equity are prioritized to maximize the benefits for all students.
References
1. Alison Cawsey "The Essence of Artificial Intelligence", Prentice Hall, First Edition, 1997, 200 pages
2. Attilio Di Battista, Sam Grayling, Elselot Hasselaar and others "Future of Jobs Report 2023", World Economic Forum, Insight Report, May 2023, 296 pages
(https://www3.weforum.org/docs/WEF Future of J obs 2023.pdf)
3. Darrell M. West "What is artificial intelligence?", 2018, (https://www.brookings.edu/articles/what-is-artificial-intelligence/)
4. Dr. Anand S. Rao and Gerard Verweij " PwC's Global Artificial Intelligence Study: Sizing the prize", PwC, 2017 (https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html)
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