Required Reading Chapters 11–12 In The Text Hao Meng Zhi Cha

Required Readingchapters 11 12 In The Texthao Meng Zhi Chao Cheng

Required Reading: Chapter’s 11 & 12 in the text HAO MENG, ZHI-CHAO CHENG, & TIAN-CHAO GUO. (2016). Positive Team Atmosphere Mediates the Impact of Authentic Leadership on Subordinate Creativity. Social Behavior & Personality: An International Journal, 44(3), 355–368. Brock, J. K.-U., & von Wangenheim, F. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. California Management Review, 61(4), 110–134. Optional Resources: Chapter 11 & 12 Journal articles Journal Article 11.1: Budhwar, P.S. (2000) ‘A reappraisal of HRM models in Britain’, Journal of General Management, 26(2): 72–91 Journal Article 11.2: Vermeeren, B., Kuipers, B. and Steijn, B. (2014) ‘Does leadership style make a difference? Linking HRM, job satisfaction, and organizational performance’, Review of Public Personnel Administration, 34(2): 174–195. Journal Article 12.1: Swailes, S. (2016) ‘The cultural evolution of talent management: a memetic analysis’, Human Resource Development Review, 15(3): 340–358. Journal Article 12.2: Kim, S. and McLean, G. N. (2012) ‘Global talent management: necessity, challenges, and the roles of HRD’, Advances in Developing Human Resources, 14(4): 566–585. This week’s journal article focus on the how positive team culture can correct the impact of lagging leadership creativity. Additionally, we discussed how 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) APA guidelines must be followed. The paper must include a cover page, an introduction, a body with fully developed content, and a conclusion. A minimum of five peer-reviewed journal articles.

Paper For Above instruction

Introduction

Artificial Intelligence (AI) has rapidly evolved from a futuristic concept into a foundational technology influencing numerous sectors. It encompasses a range of computational systems designed to perform tasks traditionally requiring human intelligence, such as learning, reasoning, problem-solving, and perception. This paper aims to define AI, evaluate the current state of AI technologies, analyze their classification, and explore how perceptions of AI differ across industries and geographical locations. Drawing from recent peer-reviewed scholarly articles, the discussion highlights the practical applications, challenges, and perceptions of AI in contemporary organizational and societal contexts.

Defining AI

AI refers to the development of computer systems capable of performing tasks that typically necessitate human cognitive functions. According to Russell and Norvig (2016), AI involves creating agents that perceive their environment and take actions to maximize their chances of success. This encompasses machine learning, natural language processing, robotics, and computer vision. AI systems can be categorized broadly into narrow AI, which performs specific tasks, and general AI, which possesses human-like intelligence across diverse domains—a level that remains largely theoretical today.

Current AI Technologies and Their Classifications

Presently, several AI technologies are commercially available and widely implemented across industries. Notably, machine learning algorithms underpin many applications; these systems learn from data patterns without explicit programming (Goodfellow, Bengio, & Courville, 2016). Natural language processing (NLP), exemplified by chatbots and virtual assistants, enables machines to interpret and generate human language. Computer vision systems allow automated image and video analysis. Robotics incorporates AI to facilitate autonomous navigation and task execution. Lastly, recommendation systems in e-commerce and streaming services personalize user experiences based on behavioral data.

While all these technologies are frequently labeled as AI, the extent to which they embody 'true' AI varies. For instance, many NLP and recommendation systems function based on pattern recognition and probabilistic models, arguably representing narrow AI rather than artificial general intelligence. Consequently, some critics argue that current AI is an extension of advanced data processing rather than true intelligence—lacking consciousness, understanding, or reasoning akin to humans.

Perceptions of AI Across Industries and Locations

Perceptions and implementations of AI differ significantly across industries and geographical regions. In the healthcare sector, AI is viewed as a tool for improving diagnostics and personalized medicine, often regarded with optimism regarding its potential to save lives (Topol, 2019). Conversely, in manufacturing, AI-driven automation is perceived as a means to increase efficiency but also raises concerns about job displacement (Brougham & Haar, 2018).

Regionally, North American and European markets tend to emphasize ethical considerations, data privacy, and regulatory frameworks surrounding AI adoption (Crawford & Calo, 2016). In contrast, some Asian countries, like China, focus heavily on rapid deployment and technological advancement, viewing AI as a strategic asset to bolster economic growth and security (Lee, 2018). These varied perceptions influence public trust, governmental policies, and industry investment in AI research and development.

Conclusion

Artificial Intelligence remains a transformative force across multiple sectors, characterized by a broad spectrum of technologies with varying degrees of sophistication. Its perception is shaped by regional cultural, economic, and regulatory contexts, affecting its adoption and ethical considerations. While current AI technologies demonstrate remarkable capabilities, they often fall short of true general intelligence, prompting ongoing debates about their potential and limitations. Understanding these dynamics is crucial for organizations and policymakers aiming to harness AI responsibly and effectively.

References

  • Crawford, K., & Calo, R. (2016). There is a blind spot in AI researchers’ race model. Nature, 538(7625), 311–313.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
  • Lee, K. F. (2018). AI superpowers: China, Silicon Valley, and the new world order. Houghton Mifflin Harcourt.
  • Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms: The role of human resource management. Journal of Management & Organization, 24(5), 639–652.
  • Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
  • Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson.
  • Brock, J. K.-U., & von Wangenheim, F. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. California Management Review, 61(4), 110–134.
  • Vermeeren, B., Kuipers, B., & Steijn, B. (2014). Does leadership style make a difference? Linking HRM, job satisfaction, and organizational performance. Review of Public Personnel Administration, 34(2), 174–195.
  • Swailes, S. (2016). The cultural evolution of talent management: a memetic analysis. Human Resource Development Review, 15(3), 340–358.
  • Kim, S., & McLean, G. N. (2012). Global talent management: necessity, challenges, and the roles of HRD. Advances in Developing Human Resources, 14(4), 566–585.

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