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Four Emerging Technologies Disrupting the Next Three to Eight Years

Emerging technologies and trends expected to dominate in the next three to eight years are poised for significant advancements. Here are four particularly noteworthy ones to keep an eye on.


No. 1: Neuromorphic computing

  • A critical enabler, neuromorphic computing provides a mechanism to more accurately model the operation of a biological brain using digital or analog processing techniques.

  • It will take three to six years to cross over from early-adopter status to early majority adoption.

  • Neuromorphic computing will have a substantial impact on existing products and markets.

Simplifying AI Development and Disrupting the Industry

Neuromorphic computing systems streamline product development, enabling real-world responsive AI systems. They offer quick reactions to real-time events and information, making them a foundation for future AI-based products. Expect breakthrough devices by 2023 and widespread adoption in five years. The impact will be significant, as neuromorphic computing offers power and performance benefits over current AI technology."



No. 2: Self-supervised learning

  • Self-supervised learning accelerates productivity by using an automated approach to annotating and labeling data.

  • It will take six to eight years to cross over from early-adopter status to early majority adoption.

  • Self-supervised learning will have a significant impact on existing products and markets.

Self-supervised learning speeds up data annotation and labeling through automation. It will reach widespread adoption in 6-8 years. Its impact on existing products and markets will be significant. It relates information by learning how one piece of data is related to others, such as situations that precede or follow and words that often appear together. Recently emerging from academia, it is currently limited to a few AI companies, with computer vision and NLP companies starting to incorporate it into their product plans. With the ability to extend machine learning to organizations with limited access to large datasets, self-supervised learning holds vast potential, particularly in computer vision and NLP applications."


No. 3: Metaverse

  • The metaverse fuels the smart world by providing an immersive digital environment.

  • It will take eight-plus years to cross over from early-adopter status to early majority adoption.

  • The metaverse will have a very substantial impact on existing products and markets.

The metaverse brings a new level of digital content that seamlessly integrates with the physical world, offering persistence, collaboration, interoperability, and decentralization. It is a result of various emerging technologies and trends such as spatial computing, digital persistence, multientity environments, decentralization, high-speed networking, sensing technologies, and AI applications. To reach widespread adoption, these supporting technologies need to reach an early majority stage. Currently, all existing examples are considered pre metaverse offerings as they are compatible and capable, but don't fully meet the definition of the metaverse. While the full potential and benefits are yet to be realized, the shift towards the metaverse is expected to be as significant as the transition from analog to digital."


No. 4: Human-centered AI

  • Human-centered AI (HCAI) is a common AI design principle calling for AI to benefit people and society, which could improve transparency and privacy.

  • It will take three to six years to reach early majority adoption.

  • HCAI will have a substantial impact on existing products and markets.

HCAI (Human-centered Artificial Intelligence) operates on the principle of collaboration between humans and AI to improve cognitive performance and enhance experiences. This approach is also known as "augmented intelligence," "centaur intelligence," or "human in the loop." It emphasizes that AI must have a goal to benefit humans, even in fully automated systems.

By embracing HCAI, AI vendors can reduce risks, act responsibly, and be more efficient in their automation processes while incorporating a human touch and common sense. Many AI companies have already adopted this approach, moving away from technology-focused AI product development.

HCAI holds great potential as it leverages human strengths to boost productivity and address limitations, biases, and blind spots in AI systems.


Font: Gartner

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