ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML)
AI provides organizations with powerful capabilities to optimize their processes and offer entirely new services. Four methods are currently being used: Supervised learning for pattern matching and recognition, prediction, and automation. Transfer learning applies these capabilities across related problems.
Reinforcement learning is goal-oriented, continuous learning, as seen in game-playing. Unsupervised learning is information synthesis, such as automated text creation. As new complimentary technologies evolve (see Quantum Computing), AI will continue to improve in performance. Artificial General Intelligence (AGI), surpassing human intelligence, is only expected to become available after 2030. However, the merits of AGI are contentious, and it is machine learning which complements rather than mimics human intelligence that will advance strongly over the next decade.
DISTRIBUTED LEDGER TECHNOLOGY (DLT)
DLT can be considered as the glue for many digital technologies, offering distributed access, secure transaction management as well as execution of smart contracts. For consumers but also for companies, DLT can power digital wallets which conveniently bundle data from various databases, including tokens, passwords, and even identity. Use cases can already be found in many industries, and the wider implementation of the technology is expected to deepen into mainstream use from 2025, although there will be some large-scale applications before then.
INTERNET OF THINGS (IOT) AND EDGE COMPUTING
The implementation of sensors virtually everywhere will not only increase our understanding of systems but also our ability to remotely operate or automate them. IoT is a key element of compositional architectures providing insights on the status of a physical asset. The dramatic increase in types of sensors and the quality of information generated has created a need for edge computing capabilities (processing the information close to the point of generation, understanding the input, and instructing systems to act if necessary). By 2030, Cisco predicts the IoT-connected devices to number 500 billion, up from some 20 billion devices in 2020.
Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform a computation. The first quantum computers were built in the late 1990s, and commercial quantum computers came to market in 2011, when D-Wave announced a 128-qubit quantum computer. While D-Wave’s approach to quantum computing focuses on optimization challenges, the universal quantum computer, as built by Google, IBM, and Intel, which applied superconducting circuits, will address a broader range of problems. As we see greater amounts of data being created, the ability to analyse and use that data to deliver insights will be key, and quantum computing promises superior capabilities.
However, there are still limitations in quantum computing today, including the ability to handle errors and maintain quantum states for extended periods. We expect evermore exciting close-up proof-of-concept quantum devices in the coming decade, but we believe no real commercial scale quantum computing applications before 2030.11
VIRTUAL REALITY (VR) / AUGMENTED REALITY (AR) / MIXED REALITY (MR)
These technologies layer digital information into either completely (VR) or partially (AR/MR) digitalized environments. The applications for VR focus on creating new environments which are not conveniently accessible (such as undersea, outer space, or hostile) or which require an entirely new spatial metaphor (such as virtual offices). Augmented reality usually applies information in a disassociated visual context, but thanks to advanced sensors such as those in the Microsoft HoloLens or Magic Leap, information can be mapped to specific physical contexts. There are challenges with their application, most notably potential motion sickness and costly hardware for both VR and MR applications. In the next ten years, we expect to see these platforms mature through the current experimentation, where the design and capabilities in the applications will become the focus.
Source : Extracts from DNVGL Technology outlook report 2030
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