Sample Technology Paper on The Rise of Artificial Intelligence

Rise of Artificial Intelligence


Numerous elements of our daily existence have changed due to the exceptional technological advancements brought about by the rise of artificial intelligence. Today’s organizations, such as healthcare, banking, public transit, and recreation, use AI technology. While the development of artificial intelligence (AI) has many advantages, it has also brought about serious issues like employment displacement, biased judgment, and legitimate implications. Concentrating on preparing individuals to work with AI and incorporating ethical concerns into AI technologies to tackle these problems is crucial.

The loss of jobs is one of the main issues raised by AI. Numerous workers are jobless as AI continues to supersede manual input in various sectors. According to Mirbabaie et al., economic and social issues brought on by this displacement must be handled (2022). The education of humans to collaborate with AI is crucial to solving this problem. Individuals can be trained in new sectors related to AI or have their current skills improved to work with AI technology as part of this education. To help the working population adjust to the shifting employment market, organizations and governments can also offer incentives and grant assistance for upgrading and retraining.

Decision-making that is prejudiced is another issue with AI. Because AI algorithms are designed to base their choices on intelligence, the AI will also decide distorted things if the data is flawed. This prejudice can have serious repercussions, particularly in professions like criminal justice, where AI is used to make choices affecting people’s lives. It is critical to ensure that the data used to teach AI systems is impartial and diverse to solve this problem. A frequent auditing process should also be mandated for companies creating AI to spot and address system errors.

Several populations, such as the workforce, are significantly impacted by artificial intelligence (AI) development. According to Mirbabaie et al., AI technology is advancing quickly. Because it can automate decisions and duties, it has the possibility of upending many markets for goods and services as well as employment (2022). Up to 375 million workers, or 14% of the labour pool, may need to change professional categories by 2030 as a result of the automation of their present jobs, as per a study by the McKinsey Global Institute (Chui et al., 2021). As a result, many people are very concerned about how AI will affect employment and job disruption, especially those employed in low-skilled roles and those lacking access to education programs.

In conclusion, while artificial intelligence has brought some advantages, it has also led to important issues requiring attention. These problems can be resolved by training people to cooperate with AI and incorporating ethical concerns into AI development.



Chui, M., Hall, B., Singla, A., & Sukharevsky, A. (2021, December 8). The state of AI in 2021. McKinsey & Company. Retrieved March 7, 2023, from

Mirbabaie, M., Brünker, F., Möllmann, N. R., & Stieglitz, S. (2022). The rise of artificial intelligence–understanding the AI identity threat at the workplace. Electronic Markets, 1-27.




The most significant difference between common and academic research sources is the authority and credibility each conveys to the researcher. The authors of literary texts are often experts in the field, and their works are constantly reviewed by peers who share their expertise. However, as most popular sources are written by journalists, bloggers, or general authors, they are not subject to further scrutiny or inspection (RMIT University, 2019). A second difference is the different amounts of the material discussed. The focus of academic sources is on study, analysis, and theory rather than a broad overview of a topic or issue.

On the other hand, popular sources provide a broader overview and are typically written in style accessible to a larger readership (Cendejas, 2014). Thirdly, everyone has their unique way of speaking and writing. Academic papers often include highly specialized terminology and jargon that might be difficult for a general reader to grasp.

The language and style used in popular publications are intentionally simple to appeal to a broader readership. Fourth, each type of source’s intended audience and purpose are different. Educational materials are written for academics and are meant to further the field of study in a particular area. Several other sorts of media are written for a broad audience. Nonetheless, famous works aim to reach a broad audience through a combination of informational value, entertainment value, and persuasion (Effective Internet Search, n.d). Lastly, the two sorts of sources are vastly different regarding accessibility. Popular sources are generally available through public libraries, newsstands, and the Internet. In contrast, scholarly sources are typically only available through academic libraries, online databases, or services that require a paid subscription.

An excellent example of an academic paper on “The Growth of Artificial Intelligence” is Chen et al.’s “Application  and theory gaps throughout the rise of artificial intelligence in education.” In this part, the writers discuss the biases of using AI in the classroom, focusing on the profiling of students and the security of their personal information. They also highlight the reliability concerns of using AI algorithms in decision-making, which might be affected by biased training data or broken algorithms. These elements influence algorithmic judgments. One of the article’s merits is its in-depth analysis of the present state of AI in classrooms. One of the article’s merits is the author’s use of many sources to show key holes in both practice and theory. The authors also propose avenues for further inquiry and technical development (Chen et al., 2020).

Nevertheless, the report has certain caveats. It only discusses one type of AI in education (machine learning), which may not accurately portray the variety of AI applications already being used. However, the authors need to perform thoroughly exponential the ethical implications of AI in education, despite guiding for addressing biases and reliability issues.

One popular article related to the topic is “How Artificial Intelligence Will Change Everything.” The article explores the potential of AI to revolutionize industries such as healthcare, transportation, and finance and the possible risks associated with the rapid development of AI. In terms of biases, the article presents a generally positive outlook on the potential impact of AI, highlighting its benefits while acknowledging the possible risks. However, the author does not extensively discuss the potential negative impact of AI on society, such as bias and discrimination in AI algorithms. The article is reliable, published in a reputable source, the Wall Street Journal, and cites experts and studies to support its claims (How Artificial Intelligence Will Change Everything, 2017). Regarding its merits, the article provides a comprehensive overview of the potential impact of AI on various industries, highlighting its potential benefits and challenges.

Regarding limitations, the article was published in 2017 and may not fully reflect the current state of AI development and its impact on society. Additionally, the article must extensively discuss AI’s ethical and social implications beyond the potential risks and challenges.

The scholarly, peer-reviewed source has five visual cues outside the more common version. Here are some indicators: the proper use of industry jargon above all else. Academic sources’ usage of buzzwords like “machine learning,” “deep learning,” and “neural networks” is far more in-depth than that of a popular source. Second, the source uses examples to illustrate points differently from the public source. Self-driving cars, virtual assistants, unsupervised learning, and reinforcement learning are just a few of the applications of AI that have been demonstrated in recent years. Thirdly, the academic source includes citations from other authors, such as Baker et al.’s (2019) “Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges” (Baker et al., 2019). Tones of seriousness are the fourth factor to think about. The scholarly source has a more serious, academic tone than the more accessible one. For example, phrases like “application and theory gaps” and “machine learning approaches” are couched in more specialized and technical terminology in the scholarly source. As a last remark, I want to emphasize the importance of paying close attention. The scholarly source, in contrast to the popular one, gives much thought to the subject of how the development of AI would affect classroom instruction.




Baker, T., Smith, L., & Anissa, N. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Retrieved May 12, 2020.

Cendejas, M. (2014, May 13). Scholarly and Popular Resources(1) – Ashford University.

Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial intelligence, 1, 100002.

Effective Internet Search: Basic Tools and Advanced. – Embedded Content – Films On Demand. (n.d.). Retrieved March 7, 2023, from

How Artificial Intelligence Will Change Everything. (2017, March 7). Wall Street Journal.

RMIT University. (2019). Why Can’t I Google It? | RMIT University. In YouTube.