This Week's Journal Article Focus On How Positive Team Cultu ✓ Solved

This Weeks Journal Article Focus On The How Positive Team Culture Can

This week’s journal article focus on how positive team culture can correct the impact of lagging leadership creativity. Additionally, we discussed digital transformation leaders in regard to artificial intelligence (AI). After reviewing the reading, please answer the following questions: What is your definition of AI? Please explain. What is your opinion of AI, is the technology currently available? Why or why not? Please note at least four AI technologies, explain if they are truly AI or something else. Thoroughly explain your answer. How is AI perceived as different in various industries and locations? Please explain.

Be sure to use the UC Library for scholarly research. Google Scholar is also a great source for research. Please be sure that journal articles are peer-reviewed and are published within the last five years. The paper should meet the following requirements: 3-5 pages in length (not including title page or references).

Paper For Above Instructions

Artificial Intelligence (AI) has emerged as a transformative technology in recent years, reshaping industries and influencing leadership dynamics in organizations. Defined broadly, AI refers to computer systems performing tasks that typically require human intelligence, including learning, reasoning, problem-solving, perception, and language understanding (Russell & Norvig, 2020). This distinction primarily hinges upon systems that can learn from experience and adapt to new inputs in their environment, bringing about various functionalities that mimic human cognitive processes.

In my opinion, AI technology is currently very much available, with diverse applications ranging from simple consumer products to complex enterprise solutions. Its rapid development has been fueled not only by a surge in data availability but also by advancements in computational power. For instance, technologies such as machine learning, neural networks, and natural language processing have become integrated into everyday applications like virtual assistants, recommendation systems, and automated analytics tools. Nevertheless, the technology still faces ethical concerns and limitations, such as biases in algorithmic decision-making and ensuring data privacy (Noble, 2018).

To illustrate the landscape of AI technologies, let’s explore four prevalent applications: chatbots, machine learning, facial recognition, and automated content generation. Chatbots are software powered by natural language processing (NLP) algorithms that facilitate conversational interfaces, assisting users with various tasks. While they can simulate conversation, the underlying technology relies on programmed responses rather than an understanding of the nuances of human communication, which places them on the border between AI and conventional programming (McTear, 2017).

Next, machine learning, a subset of AI, enables systems to learn from data and improve over time without being explicitly programmed. This technology is genuinely AI, as it embodies the core principles of mimicking human learning processes. Its applications are vast, including predictive modeling and personalized content delivery, making it a cornerstone of contemporary AI applications (Jordan & Mitchell, 2015).

Facial recognition technology exemplifies another AI application, employing deep learning algorithms to analyze and identify human faces through visuals. This technology has sparked discussions concerning privacy and surveillance, highlighting the need for ethical considerations in its deployment (Garvie et al., 2016). While genuine in its technological foundations, its use raises significant societal concerns that are crucial in the discourse around AI adoption.

Lastly, automated content generation—such as using algorithms to create articles or artistic works—can also be seen as part of the AI ecosystem. While the output may seem human-like, the technology relies on data-driven templates rather than genuine creativity. Hence, while it falls under AI, it represents limitations in true originality and intelligence (Dale, 2019).

The perception of AI varies greatly across different industries and geographical locations. In the tech industry, for instance, AI is often viewed as an enabler and a critical driver of innovation. Companies spearheading digital transformation often employ AI to streamline operations, improve customer experiences, and derive actionable insights from vast data pools (Brynjolfsson & McAfee, 2014). Conversely, in sectors such as healthcare or finance, AI may be perceived with more caution due to concerns over ethical implications and job displacement (Obermeyer et al., 2019).

Geographically, regions with high tech adoption, such as North America and parts of Asia, commonly embrace AI as a pivotal component of their economic strategy. In contrast, developing countries may view AI through a lens of skepticism, often highlighting the digital divide and concerns about equality in access to AI technologies (Chui et al., 2018). This disparity indicates that although AI holds transformative potential, societal context plays a vital role in its acceptance and integration.

In conclusion, the influence of AI is multi-faceted, characterized by its diverse applications across industries and regions. While architected to mimic human intelligence, it embodies both promise and peril, requiring careful navigation through ethical and practical considerations. As we continue to unearth the capabilities of AI, fostering a positive culture within teams can guide organizations to harness these advancements creatively and responsibly, ensuring that leadership is augmented rather than constrained by technological evolution.

References

  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
  • Chui, M., Manyika, J., & Miremadi, M. (2018). Where machines could replace humans—and where they can’t (yet). McKinsey Quarterly.
  • Dale, R. (2019). Language generation: A new frontier for AI. Communications of the ACM, 62(9), 27-29.
  • Garvie, C., Bedoya, A., & Frankle, J. (2016). The perpetual line-up: Unregulated police face recognition in America. Upturn.
  • Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
  • McTear, M. F. (2017). The rise of conversational AI. Companion Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.
  • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
  • Obermeyer, Z., Powers, B., Gottlieb, D. J., & et al. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
  • Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach. Pearson.
  • Vogels, E. A. (2020). Digital divide persists even as lower-income Americans make gains in tech adoption. Pew Research Center.