The Future is Now: Four emerging technologies will disrupt the next decade

Gartner has identified four emerging technologies that are set to revolutionize the way we live and work. This article will explore these technologies, discussing their potential impact on various industries and how businesses can prepare for adoption.

Four emerging technologies disrupting the next decade

In emerging technologies and trends, most of this year's developments are projected to take three to eight years before achieving extensive proliferation. Nevertheless, these innovations herald significant advancements in the coming years.

1. Neuromorphic computing

Neuromorphic computing, a pivotal facilitator, has the potential to simulate the functioning of a biological brain with greater accuracy through digital or analog processing techniques. However, its advent is estimated to take three to six years to transition from a niche technology to mainstream adoption.

Notwithstanding, neuromorphic computing is predicted to impact existing markets and products substantially. It simplifies the development of products, empowering leaders to design AI systems that can adeptly respond to the unpredictable nature of the real world. The autonomy of these systems enables quick reactions to real-time information and events, forming the basis for a wide range of future AI-based products. Applications such as event detection, pattern recognition, and small dataset training are already used.

Innovation in neuromorphic devices is expected to reach a breakthrough by the end of 2023, but widespread adoption will take five years. Nonetheless, the anticipated effects of this technology are significant, as it has the potential to disrupt the present AI technology advancements, delivering unparalleled power savings and performance benefits, far surpassing the current AI chips.

2. Self-supervised learning

Self-supervised learning represents a promising innovation that expedites productivity by implementing an automated data labeling and annotation approach. However, it is predicted to take approximately six to eight years to progress from early-adopter status to early majority adoption.

Undeniably, self-supervised learning will significantly impact existing products and markets. Its models learn how information correlates with other information, such as the usual order of events or the association of specific words.

Currently, self-supervised learning is still in its nascent stages and is only practiced by a limited number of AI companies. A few businesses concentrated on computer vision, and NLP products have recently included self-supervised learning in their product roadmaps.

The potential benefits and impact of self-supervised learning are far-reaching, extending the applicability of machine learning to organizations with limited access to large datasets. It is particularly relevant to AI applications that rely heavily on labeled data, notably computer vision and NLP.

3. Metaverse

The metaverse catalyzes the smart world, providing an immersive digital environment that fuels various applications.

However, its transition from early-adopter status to early majority adoption is predicted to take at least eight years.

Without a doubt, the metaverse will exert a very substantial impact on the existing products and markets. It allows for persistent, decentralized, collaborative, interoperable digital content that intersects with the physical world's real-time, spatially organized, and indexed content.

The metaverse is an example of a combinatorial trend where several vital, distinct, and independently evolving technologies and trends interact, resulting in a new trend. These emerging, supporting technologies and trends include spatial computing, the spatial web, digital persistence, multi-entity environments, decentralization tech, high-speed, low-latency networking, sensing technologies, and AI applications.

The features and functionality these Emerging Technology Trends (ETT) bring to the metaverse must reach early majority adoption to cross the chasm. We consider all current examples precursors or pre-metaverse offerings because they have the potential for compatibility and capability but do not yet satisfy the metaverse definition.

While the benefits and opportunities from the metaverse may not be immediately viable, the emergence of metaverse solutions indicates potential use cases. We anticipate the transition to the metaverse will be as transformative as the transition from analog to digital.

4. Human-centered AI

Human-centered AI (HCAI) represents a crucial design principle for AI systems that prioritize the well-being of individuals and society, potentially enhancing transparency and privacy in the process.

However, it is predicted to take approximately three to six years to achieve early majority adoption.

HCAI will have a significant impact on existing products and markets. This design principle advocates for a partnership model between people and AI to enhance cognitive performance, including learning, decision-making, and new experiences. HCAI is sometimes referred to as "augmented intelligence," "centaur intelligence," or "human in the loop." Nevertheless, in a broader sense, even fully automated systems must prioritize human benefits as a goal.

HCAI empowers vendors to manage AI risks, act ethically and responsibly, and increase efficiency with automation while complementing AI with human sensibility and common sense. Many AI vendors have already shifted their focus toward the more impactful and responsible HCAI approach. The technology-centric approach to developing AI products has resulted in numerous negative consequences, compelling vendors to reconsider their AI product strategies.

The potential impact of HCAI is considerable, as it leverages human abilities to make humans more productive and eliminate avoidable limitations, biases, and blind spots.