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Introduction
Published in Chandrasekar Vuppalapati, Democratization of Artificial Intelligence for the Future of Humanity, 2021
Gartner Hype cycle for AI provides technology expectations with respect to time period that a particular AI technology is either in one of five phases (years to mainstream adoption): innovation trigger, peak of inflated expectation, trough of disillusionment, slope of enlightenment, and plateau of productivity. In other words, the Hype curve tracks the progress of emerging technology trends from early innovation to becoming a household must-have.73 For Chief Information Officer (CIO) teams, the Hype cycle provides a guidance of new technology landsrape that would be in the market in the immediate future so that proper technology scoping and preparation could be planned. The Hype cycle can serve as an early warning system for executives, and at least jumping on technologies that serve business objectives in two to five years could eventually pay big dividends [33, 34]. For Data Scientists end Data Engineers the review of Gartner Hype curve would provide an informative direction on marketability of technology that is immediately in peak of expectation and with a short-term (five years) visibility on a newer technological transition to be ealring place.
The digital century
Published in William Sarni, Digital Water, 2021
The Gartner Hype Cycle is divided into a series of five phases that represent distinct levels of maturation, expected market adoption timeline, and public perception of the technology.5
How will the diffusion of additive manufacturing impact the raw material supply chain process?
Published in International Journal of Production Research, 2020
Maximilian Kunovjanek, Gerald Reiner
The adoption of AM and its impacts on manufacturing and especially the supply chain has faced considerable hype throughout the different development stages of the technology. The so-called Gartner Hype Cycle says that most emerging technologies go through something called a ‘Peak of Inflated Expectations’, a phase during which the technology faces overenthusiasm and unrealistic projections (Walker 2017). Therefore, during this section, the authors not only analyse the relevant literature in the field but also distinguish between articles that clearly provide evidence and those that merely suggest certain outcomes. To be able to answer the research question however, it is necessary to analyse the literature with a focus on materials saving in regard to the supply chain related impacts, the degree of adoption of AM, the proportion of total manufacturing that could potentially be substituted by AM, and the so-called Saving Factor, which depicts the potential materials saving.