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Key Takeaways from Gartner’s "Hype Cycle for Emerging Technologies" 2017

Edge Computing and Gartner’s Hype Cycle for Emerging Technologies 2017

The Swim team is always on the lookout for technologies will benefit the growth of the Internet of Things (IOT), especially ones related to Industrial IOT. We’ve previously written about Edge Analytics, and have highlighted Four RFID + Edge Computing Use Cases. Recently, Gartner published their Hype Cycle for Emerging Technologies annual report, weighing in on industry buzz and emerging trends to identify the most significant technologies for 2018. Among the technologies being most hyped for 2018, Gartner has chosen to include Edge Computing for the first time.

Key Takeaways from Gartner's "Hype Cycle of Emerging Technologies" 2017 | Swim Inc.

According to the report, Edge Computing technologies are at the dividing line between being an “innovation trigger” and the “peak of inflated expectations.” This suggest that Edge Computing is primed to shake up the IOT landscape in the relatively near future. Gartner estimates that we are 2-5 years away from Edge Computing being adopted by the mainstream.

It makes sense that Gartner positioned Edge Computing closer to the “innovation trigger” category, as it has the potential to enable several of the other “peak hype” technologies that Gartner included in this year’s Hype Cycle. For example, Edge Computing provides a way to ingest, process, and reduce high volumes of data created at the IOT edge, enabling applications to perform Machine Learning (ML), Deep Learning, and Augmented Data Discovery in-context, where the data is being generated. This eliminates some of the cost and complexity of performing similar analysis in the cloud, and also makes it easier to utilize insights derived from Deep Learning analysis in applications.

Gartner "Hype Cycle for Emerging Technologies" 2017 | Swim Inc.

Gartner "Hype Cycle for Emerging Technologies" 2017

Gartner predicts deep-learning applications and tools will be a standard component in 80% of data scientists’ toolboxes by the year 2018. The ability to use these tools at the IOT edge (via Edge Computing) will empower Deep Learning, ML, and other Augmented Data Discovery applications to learn on in-production data in real-time, and enable data scientists to directly apply learned insights in IT and OT contexts. Furthermore, Edge Computing, along with ML, Deep Learning, and other forms of Augmented Data Discovery will comprise the toolset that provide the foundation for Artificial Intelligence (AI) advancements.

According to Gartner, “artificial intelligence technologies will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks; these will enable organizations with AI technologies to harness data in order to adapt to new situations and solve problems that no one has ever encountered previously.” Edge Computing will continue to be critical to the development of Artificial Intelligence technologies, by unlocking the value in the “near-endless amounts of data” generated by IOT sources. By reducing raw IOT data in real-time, Edge Computing architectures will be able to provide richer, more contextual data to AI applications while improving overall efficiency by decreasing the amount of data AI services must process.

In addition to Edge Computing, the other 7 technologies making their debut on Gartner’s Hype Cycle for Emerging Technologies 2017 list this year included 5G, Artificial General Intelligence, Deep Learning, Deep Reinforcement Learning, Digital Twin, Serverless PaaS, and Cognitive Computing.

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Learn how Swim ESP™ can empower IOT applications by using Edge Computing to optimize and alleviate your IOT data overload. Swim ESP can help transform and manage your IOT sensor data using Machine Learning on your edge devices. Transform your flood of data into actionable business insights.

Source: Gartner Hype Cycle for Emerging Technologies, 2017

Posted on Sep 6, 2017 11:25:06 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Edge Analytics