Reading Resources Brock J. K. U. Von Wangenheim F. 2019 Demy

Reading Resourcesbrock J K U Von Wangenheim F 2019 Demyst

Reading Resources: 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. Research Paper: This week’s journal article focuses on how positive team culture can correct the impact of lagging leadership creativity. Additionally, we discussed how digital transformation leaders regard 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. Google Scholar is 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+ 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. The writing should be clear and concise. Headings should be used to transition thoughts. Don’t forget that the grade also includes the quality of writing. Note: plagiarism check required, APA7 format, include References, within 8hrs

Paper For Above instruction

Artificial Intelligence (AI) has become a central component of the ongoing digital transformation across multiple industries. It refers to the development of computer systems that can perform tasks traditionally requiring human intelligence, such as learning, reasoning, problem-solving, perception, and language understanding (Russell & Norvig, 2016). AI encompasses a broad spectrum of technologies that aim to simulate or mimic human cognitive functions, enabling machines to perform complex tasks more efficiently than humans in some contexts. This paper explores my personal definition of AI, evaluates the current state of AI technologies, examines four specific AI applications to determine whether they qualify as true AI, and discusses the perception of AI across different industries and geographic locations based on recent scholarly work.

My Definition of AI

My understanding of AI is rooted in its capacity to enable machines to not only interpret data but also make autonomous decisions based on that data. Unlike traditional software that follows pre-programmed instructions, AI systems adaptively improve their performance through learning algorithms such as machine learning and deep learning (Goodfellow, Bengio, & Courville, 2016). For instance, AI-based applications like voice assistants or recommendation systems utilize vast amounts of data and complex algorithms to provide context-aware outputs that evolve as more data is processed. Fundamentally, I see AI as a set of technologies that create intelligent systems capable of performing tasks that would typically require human intellect, with the potential for continuous improvement and adaptation over time.

Opinion on the Current State of AI

The current state of AI is quite advanced but also presents significant limitations. Technologies such as machine learning, natural language processing, computer vision, and robotics are increasingly integrated into everyday life, from virtual assistants like Siri and Alexa to autonomous vehicles and sophisticated data analytics. However, these systems are often characterized as narrow AI because they excel in specific tasks but lack general intelligence—the ability to perform a wide range of intellectual tasks at a human level. While AI systems have demonstrated remarkable progress in tasks such as facial recognition and language translation, they still struggle with understanding context beyond their training data, exhibiting biases and sometimes producing unpredictable outcomes (Russell, 2019). Hence, while AI is technologically impressive, it does not yet possess fully autonomous or human-like intelligence.

Four AI Technologies and Their Classification

The following are four prevalent AI technologies, with an explanation of whether they qualify as true AI or not:

1. Machine Learning (ML): ML involves algorithms that enable computers to learn from data and improve their performance over time without explicit programming. ML is generally considered true AI because it embodies the core principle of autonomous learning and decision-making based on data patterns (Jordan & Mitchell, 2015).
2. Natural Language Processing (NLP): NLP enables machines to understand, interpret, and generate human language. While advanced NLP models like GPT-3 exhibit impressive language generation capabilities, they do not possess genuine understanding or consciousness, thus falling into narrow AI rather than true AI (Vaswani et al., 2017).
3. Computer Vision: This technology allows machines to interpret visual information from images or videos. Deep learning-based computer vision systems can perform tasks like object detection with high accuracy, but they lack general visual cognition or contextual understanding, making them narrow AI (Krizhevsky et al., 2012).
4. Rule-Based Expert Systems: These systems use predefined sets of rules to simulate decision-making in specific domains. They are not considered true AI because they rely entirely on human-crafted rules rather than learning or adapting over time (Luger & Stubblefield, 2012).

In summary, ML and NLP are closer to true AI due to their capacity for autonomous learning and adaptability, whereas rule-based systems and basic computer vision are more specialized and lack general intelligence.

Perception of AI in Various Industries and Locations

The perception and adoption of AI vary significantly across industries and geographic regions. In highly developed economies like the United States and Western Europe, AI is viewed as a strategic tool for enhancing productivity, innovation, and competitive advantage (Brynjolfsson & McAfee, 2017). For example, in healthcare, AI applications such as diagnostic imaging and predictive analytics are rapidly gaining acceptance, often seen as revolutionary advancements. Conversely, in developing countries or industries with limited technological infrastructure, AI may be perceived as a futuristic or aspirational technology, hindered by cost constraints and lack of skilled workforce (Consulting, 2020).

Furthermore, cultural attitudes toward automation influence AI acceptance. In some Asian countries, such as Japan and South Korea, there is a relatively positive perception of AI as a means to compensate for demographic challenges and labor shortages (Kim, Lee & Park, 2019). Meanwhile, concerns about job displacement and privacy issues foster skepticism in North America and parts of Europe. These perceptions are shaped by factors including government policies, educational investments, and societal values concerning technology and employment.

Overall, the perception of AI is context-dependent, rooted in technological maturity, economic priorities, and cultural attitudes. As AI continues to evolve, its perception will likely become more nuanced, emphasizing responsible development and ethical considerations.

Conclusion

In conclusion, artificial intelligence represents a revolutionary suite of technologies capable of transforming industries and societal functions. While current AI systems excel in narrow tasks such as data analysis, natural language understanding, and visual recognition, they lack the broad, adaptable intelligence characteristic of humans. Understanding the distinction between different AI technologies is essential for realistic expectations and ethical deployment. Furthermore, perceptions of AI are deeply influenced by regional, cultural, and industrial contexts, shaping the pace and manner of adoption. As scholarly research progresses, it remains vital to critically evaluate AI's capabilities and limitations, ensuring its development aligns with societal values and priorities.

References

• Brynjolfsson, E., & McAfee, A. (2017). The Business of Artificial Intelligence. Harvard Business Review, 95(4), 3-11.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
• Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6), 84-90.
• Kim, S., Lee, K., & Park, M. (2019). Cultural Attitudes and Perceptions Toward AI: A Comparative Study. Journal of Asian Business and Marketing, 11(1), 23-39.
• Luger, G. F., & Stubblefield, W. A. (2012). Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Addison-Wesley.
• Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
• Russell, S. (2019). Human Compatible: Artificial Intelligence and the Control Problem. Penguin Books.
• Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30, 5998-6008.