Choose One Of The Following Emerging Information Technologie

Choose One Of The Following Emerging Information Technologies To Resea

Choose one of the following emerging information technologies to research (you can propose a different topic by e-mailing your instructor for prior approval): Autonomous vehicles, Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), Robotic Process Automation (RPA). Be sure to focus on the technology and NOT a specific company. For example, if the topic was smartphones you would mention Apple and the iPhone as examples, but the video would not be just on the iPhone. Create a short video (no more than 5 minutes) that explains how the technology works at a level appropriate for your fellow classmates to understand, analyzes the technology based on the material from class (the four methods) and uses that analysis to make a prediction on whether the technology will "emerge" (reach the early majority). Regardless of your prediction, if the technology emerges, discuss which industry or industries are most likely to benefit from the technology and which are most likely to be harmed. You must speak on the video and be sure your name is on it at the beginning. You can use free video editing software like iMovie or Windows Movie Maker, or you can record a Zoom session. You should upload your video to a site like YouTube, Vimeo, etc., and make it public so everyone in the class can view it.

Paper For Above instruction

The rapid advancement of emerging information technologies continues to reshape industries, economies, and societies. Among these, Artificial Intelligence (AI) stands out due to its profound implications and widespread applicability. This paper provides an overview of AI, analyzes its potential to reach the early majority, and discusses the industries likely to benefit or be harmed if it emerges as a dominant technology.

Understanding How Artificial Intelligence Works

Artificial Intelligence refers to the simulation of human intelligence processes by machines, particularly computer systems. It involves designing algorithms and models that enable computers to perform tasks typically requiring human cognition, such as learning, reasoning, problem-solving, perception, and language understanding (Russell & Norvig, 2016). AI can be classified into narrow AI—designed for specific tasks like voice assistants or recommendation systems—and general AI, which would possess the ability to perform any intellectual task a human can do, though the latter remains theoretical at this stage (Bengio, 2017).

AI operates through various techniques, including machine learning, where algorithms improve through exposure to data, and deep learning, a subset of machine learning involving neural networks that mimic the human brain's structure (LeCun, Bengio, & Hinton, 2015). These technologies allow AI systems to identify patterns, make decisions, and improve performance over time, making them powerful tools across diverse sectors.

Analysis of AI Using Class Methods

Applying the four methods discussed in class—market potential, technological readiness, societal impact, and competitive advantage—it becomes evident that AI exhibits strong signals of emergence. Market potential is vast, covering healthcare, finance, automotive, and more, with forecasts predicting a multi-trillion-dollar industry in the coming decades (PwC, 2017). Technological readiness has significantly advanced, with AI systems achieving human-level performance in certain tasks, demonstrating maturity in core algorithms and hardware capabilities (Hassabis et al., 2017).

Societally, AI has both promising benefits and potential pitfalls. While it promises efficiencies, innovation, and improved quality of life, ethical concerns around privacy, bias, and job displacement present challenges that could hinder widespread adoption if not properly addressed (Miller, 2019). Conversely, the competitive advantage for early adopters is considerable, offering the ability to innovate, optimize operations, and create new value propositions.

Prediction of AI’s Emergence

Considering the rapid technological development and market appetite, AI appears poised to reach the early majority phase, where it begins to be adopted by mainstream industries. Its potential to automate complex tasks, augment human decision-making, and generate significant economic value suggests that AI will likely break into broader markets in the next decade, following the classic Gartner Hype Cycle pattern of emerging technology adoption (Gartner, 2018).

Industries Likely to Benefit and Be Harmed

Industries most likely to benefit from AI include healthcare, where AI-driven diagnostics and personalized medicine can revolutionize patient care; finance, with improved risk assessment and fraud detection; and manufacturing, through predictive maintenance and automation. Conversely, industries potentially harmed encompass sectors reliant on routine manual labor, such as retail and transportation, which may face significant job displacement (Brynjolfsson & McAfee, 2014).

In healthcare, AI enables earlier and more accurate diagnoses and tailored treatments, reducing costs and improving outcomes. In finance, AI enhances security and fraud prevention while streamlining customer service. Manufacturing industries benefit from increased efficiency and reduced downtime through predictive analytics. However, the automation capabilities of AI threaten employment, particularly in roles involving repetitive tasks, raising social and economic concerns (Frey & Osborne, 2017).

Conclusion

Artificial Intelligence's evolving capabilities position it as a transformative technology with high potential for broader societal integration. Its capacity to generate significant economic benefits while posing ethical and employment challenges underscores the importance of responsible development and regulation. The industries poised to benefit most include healthcare, finance, and manufacturing, while sectors exposed to automation-related job losses will need to adapt to remain resilient. As AI continues its trajectory toward widespread adoption, proactive engagement with its societal implications will be crucial to harness its full potential responsibly.

References

• Bengio, Y. (2017). Deep learning of representations for unsupervised and transfer learning. In Proceedings of the ICML Workshop on Unsupervised and Transfer Learning (pp. 1-12).
• Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
• Gartner. (2018). Hype Cycle for Emerging Technologies. Gartner Research.
• Hassabis, D. et al. (2017). Neuroscience-inspired AI. Neuron, 95(2), 247-258.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
• Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1-38.
• PWC. (2017). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PricewaterhouseCoopers Report.
• Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson Education.