Number 1 emerging technology is neuromorphic computing. The BRN shorters are playing with a volcano about to erupt.
What’s on the 2023 Gartner Emerging Technologies and Trends Impact Radar?
These trends surfaced in our 2023 Gartner Emerging Technologies and Trends Impact Radar, which highlights 26 emerging trends and technologies to which vendors must respond, whether they are a new or established player in that space.
The Impact Radar portrays the maturity, market momentum and influence of technologies, making it a handy tool for product leaders to identify and track the technologies and trends that will help them improve and differentiate their products, remain competitive and capitalize on market opportunities.
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Four Emerging Technologies Disrupting the Next Three to Eight Years
Most of this year's emerging technologies and trends are three to eight years away from reaching widespread adoption but represent significant innovation in the years ahead.
Let’s look at four we think will prove especially interesting.
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.
Neuromorphic computing systems simplify product development, enabling product leaders to develop AI systems that can better respond to the unpredictability of the real world. Their autonomous capabilities quickly react to real-time events and information, and will form the basis of a wide range of future AI-based products. Early use cases include event detection, pattern recognition and small dataset training.
We expect breakthrough neuromorphic devices by the end of 2023, but it will likely take five years for these devices to reach early majority adoption.
The impact is likely to be significant, though, as neuromorphic computing is expected to disrupt many of the current
AI technology developments, delivering power savings and performance benefits not achievable with current generations of AI chips.
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 models learn how information relates to other information; for example, which situations typically precede or follow another, and which words often go together.
Self-supervised learning has only recently emerged from academia and is currently practiced by a limited number of AI companies. A few companies focused on computer vision and NLP products have recently added self-supervised learning to their product roadmaps, however.
The potential impact and benefits of self-supervised learning are extensive, as it will extend the applicability of machine learning to organizations with limited access to large datasets. Its relevance is most prominent in AI applications that typically rely on labeled data, primarily computer vision and NLP.
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 enables persistent, decentralized, collaborative, interoperable digital content that intersects with the physical world’s real-time, spatially organized and indexed content.
It is an example of a combinatorial trend in which a number of individually important, discrete and independently evolving trends and technologies interact with one another to give rise to another trend. The emerging, supporting technologies and trends include (but are not limited to) spatial computing and the spatial web; digital persistence; multientity environments; decentralization tech; high-speed, low-latency networking; sensing technologies; and
AI applications.
The features and functionality these ETT bring to the metaverse will need to reach an early majority in order for the metaverse to cross the chasm. We consider all current examples to be precursors or premetaverse offerings because they are potentially capable and compatible but do not yet meet the definition of the metaverse.
While the benefits and opportunities from the metaverse are not immediately viable,
emerging metaverse solutions give an indicator of potential use cases. We expect the transition toward the metaverse to be as significant as the one from analog to digital.
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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 assumes a partnership model of people and AI working together 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,” but in a wider sense, even a
fully automated system must have human benefits as a goal.
HCAI enables vendors to manage AI risks, and to be ethical, responsible and more efficient with automation, while complementing AI with a human touch and with common sense. Many AI vendors have already shifted their positions to the more impactful and responsible HCAI approach. The technology-centric approach of developing AI products has led to numerous negative impacts, urging vendors to rethink their AI product strategies.
The potential impact of HCAI is high because it leverages human abilities to make humans more productive and remove avoidable limitations, biases and blind spots.
In short:
- The Gartner Emerging Tech Impact Radar highlights the technologies and trends that have the most potential to disrupt a broad cross section of markets.
- The trends are organized around four key themes, which are critical for product leaders to evaluate as part of their competitive strategy.
- Product leaders must explore these technologies now to capitalize on market opportunities.
Tuong Nguyen is a Director Analyst within the Emerging Technologies and Trends team in Gartner Research. He undertakes analysis on immersive technologies, metaverse, computer vision, SLAM and human-machine interfaces. He advises tech provider product leaders how to factor emerging tech and trends into creating and evolving highly successful product offerings.
