Gartner Releases AI Hype Cycle: Edge AI and AutoML Among the First to Achieve Commercialization

Recently, Gartner, a long-established IT research and analysis firm, released the latest edition of its Hype Cycle for Artificial Intelligence, focusing on three key topics: the current positioning of AI, its future development trajectory, and the pathway to realizing that future.

At the peak of the curve, we can see that 12 technologies, including AI PaaS (Artificial Intelligence Platform as a Service) and AutoML (Automated Machine Learning), have become people’s greatest expectations for AI at this stage.

In contrast, Robotic Process Automation (RPA) software, GPU accelerators, and speech recognition are poised to be the AI initiatives with the fastest implementation timelines.

However, the technical feasibility of Artificial General Intelligence (creating human-like AI), Autonomous Vehicles, and Quantum Computing remains to be evaluated, and their realization may still require more than a decade.

Before understanding the 2019 AI Hype Cycle, let us first review what Gartner predicted in its 2018 Hype Cycle.

By comparing the two curves, we can observe that seven technologies—Ensemble Learning, Virtual Reality, Knowledge Management Tools, Commercial UAVs, Crowdsourcing, and Human-in-the-Loop—have disappeared from the 2019 Hype Cycle.

Conversely, eight new technologies—AI Marketplaces, Reinforcement Learning, Decision Intelligence, Data Labeling and Annotation Services, Explainable AI, Edge AI, AutoML, and Insight Engines—have made their debut on the latest edition of the AI Hype Cycle.

Certain technologies appear on both curves, but their positions have shifted significantly. For instance, the expected timeline for the realization of Augmented Reality has improved substantially, moving from the trough of disillusionment to the slope of enlightenment. Meanwhile, Autonomous Vehicles, which have recently seen widespread activity across various countries worldwide, have faced a dual blow of declining expectations and delayed implementation timelines.

Regarding the long-standing commercialization challenges plaguing AI enterprises, Gartner believes that custom-developed AI solutions, AI cloud services and APIs, search and insight engines, AI embedded in ERP, CRM, and HR applications, and automated ML will be the first to gain enterprise adoption, emerging as the technologies with the highest potential for commercialization.

However, to realize the aforementioned technologies, as well as other more distant ones mentioned in the curve, it is crucial to establish ethical and moral frameworks. Particularly in the face of artificial intelligence, we may be confronted with an unprecedented cognitive revolution.