The concept of ‘hype’ is widely used in the business and public sphere and serves as a way to characterize increasing expectations of developments in technological fields. This paper seeks to analyze a ‘hype in the making’ by closing in on a case at the intersection of data science and energy. Following the previous body of literature qualitative as well as quantitative indicators are taken into account in order to assess the promises, hope and hype of the optimization of datacenters through machine learning. The analysis concludes that this techonogy is nearing its peak of expectation but shows favorable signs for activities after disappointment.
Table of Contents
1. Abstract
2. Hyping Smart Energy
3. The Gartner Hype Cycle
4. Learning Energy
5. Cycle of Hype
6. Conclusion
7. Bibliography
Objectives and Topics
This paper aims to analyze the "hype in the making" surrounding the application of machine learning to optimize energy usage in datacenters. By synthesizing qualitative discourse analysis with quantitative indicators, the research assesses the discrepancy between technological promises and current realities within the context of the Gartner Hype Cycle.
- Analysis of technological hype cycles and expectation management.
- Evaluation of machine learning applications in self-regulating energy systems.
- Investigation of Google Deepmind’s datacenter optimization as a case study.
- Examination of global search interest and academic citation trends regarding machine learning.
- Assessment of the sustainability and long-term potential of AI-driven energy management.
Excerpt from the Book
3. The Gartner Hype Cycle
The Gartner Hype Cycle is a visual representation of the maturity, adoption and social application of specific technologies, developed by the American ICT consultancy Gartner Inc. in 1995. The framework merges the expectations directed to a given technology, which are initially very high but decrease after disappointment, with the engineering or business maturity of the technology which follows the classical technology S-curve (Dedehayir and Steinert, 2016; Figure 1). The initial graph, which develops similar to a bell curve is the expression of the irrational, overconfident and uneducated expectation novel technologies can attract, which after an initial run up get confronted by engineering reality, which cannot follow up on the initial expectations. Fenn and Raskio (2008) find that this is due to the underlying human characteristics of attraction to novelty, social contagion and heuristic attitude in decision making. This phase of hype is crucial as it attracts a vast amount of attention and can lead to decisions that might seem unjustified when the real potential of any given technology becomes apparent later on.
This second phase of growth towards technological maturity follows the path of an S-curve. This is a classical conception of emerging technologies. In the beginning fundamental parameters are only understood by few people and only with limited detail. Through initial prototypes and pilots or early adopters progress is made until a certain threshold of knowledge, interest and dissemination is reached to facilitate a comparably rapid advance is made possible. After this rise in sophistication, the ‘low hanging fruits’ are fleshed out, development becomes harder again and physical or economical limitations kick in, which flattens out the rate of progress thereby completing the S-curve (Dedehayir and Steinert, 2016).
Summary of Chapters
1. Abstract: Provides an overview of the paper's goal to analyze the hype surrounding machine learning in energy optimization using both qualitative and quantitative indicators.
2. Hyping Smart Energy: Discusses the societal shift toward an information society and how technological optimism often creates unrealistic expectations regarding emerging innovations.
3. The Gartner Hype Cycle: Explains the conceptual framework used to visualize the maturity and adoption of new technologies through a combination of expectations and engineering reality.
4. Learning Energy: Explores the evolution of machine learning and its application in reshaping infrastructure, specifically focusing on energy network management.
5. Cycle of Hype: Applies the hype framework to the specific case of Google Deepmind’s datacenter optimization and analyzes search interest and academic output trends.
6. Conclusion: Synthesizes the findings, suggesting that while machine learning is vital for energy management, it is currently subject to an overextension of expectations.
7. Bibliography: Lists the academic literature and reports referenced throughout the study.
Keywords
Machine Learning, Gartner Hype Cycle, Smart Energy, Artificial Intelligence, Datacenter Optimization, Technological Hype, Big Data, Energy Networks, Innovation, Sustainability, Climate Change, Digitalization, Technology Adoption, Expectation Management, Neural Networks.
Frequently Asked Questions
What is the primary focus of this paper?
The paper examines the phenomenon of "hype" surrounding the application of machine learning technologies, specifically focusing on the intersection of data science and energy management in datacenters.
What are the central themes discussed in this work?
The central themes include technological life cycles, the societal impact of artificial intelligence, the disconnect between technological promise and implementation, and the quantitative analysis of public and academic interest in emerging tech.
What is the main research question or goal?
The goal is to determine whether the current enthusiasm for machine learning in energy systems represents an over-hyped phase and to assess the potential for long-term viability after expected market disillusionment.
Which scientific method is utilized in this study?
The author employs a mixed-methods approach, combining qualitative discourse analysis of whitepapers and technological promises with quantitative analysis of Google Trends search patterns and academic citation data.
What topics are covered in the main section of the book?
The main sections cover the definition of the Gartner Hype Cycle, the evolution of machine learning from rule-based systems to neural networks, and a specific case study of Google Deepmind’s datacenter efficiency projects.
Which keywords best characterize this research?
The study is characterized by terms such as Machine Learning, Gartner Hype Cycle, Smart Energy, and technological expectations.
How does the author define the "hype" around machine learning?
The author defines it as an "overextension of expectations" fueled by human attraction to novelty, which is then confronted by the practical, physical, and economic limitations of engineering reality.
What does the case study of Google Deepmind reveal?
The case study illustrates that while machine learning can indeed improve energy efficiency in datacenters, the public discourse surrounding it often portrays it as a "savior of climate change," a promise that may be overly ambitious.
Why are citations per publication decreasing despite high interest?
The author speculates that this trend might be a symptom of increased academic pressure to publish in popular domains, potentially regardless of the actual quality or relevance to long-term technological progress.
- Citar trabajo
- M.A. Stefan Raß (Autor), 2020, Learning Energy. Promises, Hope and Hype in the Context of Machine Learning, Múnich, GRIN Verlag, https://www.grin.com/document/1001869
