Staying Ahead: Technological disruptions to keep a close eye on

The 2023 Gartner Emerging Technologies and Trends Impact Radar outlines some key technologies and trends for the next few years. Read on to know more:

Keeping up with the latest technologies and trends is essential for staying competitive and capitalising on market opportunities in a fast-paced business world. In this regard, the 2023 Gartner Emerging Technologies and Trends Impact Radar can be a valuable tool for product leaders to identify and track the technologies and trends that will help them improve and differentiate their products.

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 and remain competitive. In this article, we will take a closer look at a few emerging technologies and trends that are expected to have a significant impact on existing products and markets over the next three to eight years.

Neuromorphic Computing: The Future of AI

Neuromorphic computing is a critical enabler that provides a mechanism to more accurately model the operation of a biological brain using digital or analog processing techniques. This technology is expected to take three to six years to cross over from early-adopter status to early majority adoption. Such 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.

Gartner expects 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.

Self-Supervised Learning: Automating Data Labelling

Self-supervised learning accelerates productivity by using an automated approach to annotating and labelling data. It is expected to take six to eight years to cross over from early-adopter status to early majority adoption. Such 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 practised 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 organisations with limited access to large datasets. Its relevance is most prominent in AI applications that typically rely on labelled data, primarily computer vision and NLP.

Metaverse: The Convergence of Virtual and Physical Reality

The metaverse refers to a virtual shared space, created by the convergence of virtually enhanced physical reality and physically persistent virtual reality. It is expected to have a significant impact on various industries, such as entertainment, gaming, and e-commerce. As the metaverse continues to evolve, it will become an increasingly important platform for businesses to engage with customers, creating new business and monetization opportunities. The metaverse will also provide new opportunities for data collection and analysis, enabling businesses to gain valuable insights into customer behaviour and preferences.

Differential Privacy: Protecting Sensitive Data

Differential privacy is a method for protecting sensitive data by adding noise to the data in such a way that it does not reveal individual identities while still allowing for accurate analysis. It is expected to be particularly important as more organisations collect and analyse large amounts of personal data. As data privacy concerns continue to grow, businesses will need to find ways to protect the sensitive information they collect while still gaining valuable insights from that data. Differential privacy provides a solution by allowing organisations to analyse data without revealing individual identities, thus providing a balance between data privacy and data utility.

With the increasing amount of data being collected and analysed, businesses will need to find ways to protect the sensitive information they collect while still gaining valuable insights from that data. Differential privacy provides a solution by allowing organisations to analyse data without revealing individual identities, thus providing a balance between data privacy and data utility.

Human-centred AI

Human-centred AI (HCAI) is a design principle that focuses on AI benefiting people and society. This approach improves transparency and privacy and is expected to be adopted by the majority of companies in 3-6 years. It involves a partnership between people and AI to enhance cognitive performance and is often referred to as “augmented intelligence” or “centaur intelligence”. HCAI enables companies to manage AI risks and be more ethical and efficient with automation, while also keeping a human touch. Many AI companies have already shifted to this approach due to negative impacts caused by a technology-centric approach. HCAI has the potential to make humans more productive and remove limitations, biases and blind spots.

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