Discovery – Technology Trends

I’ve picked out fourteen of the current technology trends that I think are interesting for a variety of reasons.

14 current technological trends

Here’s an initial overview of each of these. I’ll be adding in more detail over coming months.

Internet of Things (IoT)

The IoT is a clumsy name for the natural evolution of the Internet, which has always been about interconnected devices and the flow of data between them. Many technology areas outlined below could be recast as applications of the IoT in specific domains: a growing number of devices able to communicate with us and with each other, acquiring, storing, sharing, analysing and acting upon data


Automation is prevalent in many manufacturing environments, but increasingly impacts other areas, such as the routine processes previously undertaken by office workers. This includes the capture and analysis of information as much as in how that information is processed, taking us beyond the Victorian metaphors of “filing” and “forms” which still dominate much of the design and application of technology in many environments

Connected mobility

Apps such as Citymapper provide insight into improved ways of seamlessly navigating from A to B (or variants such as A to C via B) using multiple modes of transportation. Such apps will increasingly interact with transportation itself (such as lorries, buses and cars) rather than simply monitoring it, enabling transport to dynamically reconfigure and reschedule itself in order to move people and goods around much more effectively and efficiently

Artificial Intelligence (AI)

In specific application areas—notably speech recognition and facial recognition—AI is becoming more proficient. Often this is the result of improved computational power rather than because of fundamental improvements in the underlying theoretical models. AI is also beset by bias and failures (false positives and negatives), either as a result of inadequate or flawed data during its training and application, or via flaws in the underlying models. The idea of generalised AI, however, remains as remote as ever

Machine Learning (ML)

ML provides more efficient ways of identifying and analysing patterns in data and of extrapolating potential insights. Pattern recognition, data mining, and data analytics are being employed in a variety of domains, including speech and facial recognition as well as medical diagnosis and financial systems

Smart cities

Much so-called “smart city” infrastructure is relatively dumb consisting of public access Wi-Fi points, CCTV cameras, facial recognition, etc. In the future, there will be much more self-reporting and AI-enabled analytics and reconfiguration of the infrastructure of the cities themselves, with the intention of making them safer and more desirable environments. However, current poor design is raising many security and privacy concerns and potentially leaving critical infrastructure vulnerable to manipulation or cyber attacks

Agents and algorithms

Current lifestyle apps monitor basic health information (heartbeat, exercise levels, hours of sleep, etc.) or require manual input of other data (diet, drink, health tests). These data flows are increasingly automated and feed agents and algorithms that aim to dynamically maintain or improve our health and wellbeing, identifying anything out of the norm, “nudging” us whether we want to be or not out of deemed “bad” habits. The collective data and insight this provides aims to improve societal health—provided that issues of privacy and anonymity can be addressed, along with issues of social exclusion for those who cannot or will not use technology, particularly if it is perceived as invasive and untrustworthy

Augmented Reality (AR) / Virtual Reality (VR)

Although primarily seeded in gaming and entertainment areas, AR and VR have expanded into education, training and areas such as rehabilitation. In medicine, they are enabling doctors to undertake or rehearse surgery (including at a distance from the patient, when also assisted by high fidelity robotics)

Databases, distributed ledgers and blockchain

Despite the hype, Blockchain has yet to establish true value outside of cryptocurrencies. The original Blockchain was designed for natively digital assets in untrusted environments. However, most environments deal with physical assets and involve trusted entities (notably government environments, which have trusted institutions, such as the Land Registry or DVLA to operate them). Of potential wider application are distributed ledgers, which enable trusted parties to update and share information more effectively

Cloud and serverless

Cloud enabled many organisations to adopt compute-on-demand and application-on-demand services. This trend will increase, just as central generation and distribution of electricity replaced bespoke localised approaches. “Serverless” and re-usable functions have taken cloud to the next level of on-demand granular services

Edge computing

Running counter to the cloud approach (centralising computational resources), edge computing brings computation and data storage closer to the locations where they’re needed, improving response times and saving bandwidth. The IoT, particularly in smart environments, is an example of where processing and data can operate locally within the edge environment (although some IoT devices also depend upon cloud computing services). It also offers some potential attractive mitigations to central surveillance and tracking of devices, users and data. One area of focus is determining the optimal place for code to run and data to be stored and accessed—edge or centre.

Smart spaces

Smart spaces involve the deployment of multiple, potentially interacting technologies such as smart speakers, smartphones and smart watches (and other wearables) together with smart lightbulbs and thermostats. Smart spaces also interweave with other technology strands, such as AI, AR/VR, cloud and edge computing, providing us with more personalised, adaptive environments

Security, privacy and identity

Technology needs to be secure by design otherwise our infrastructure will be vulnerable to accidental or malicious abuse. This covers security of data—whether public or private—as well as security of devices (hardware), the software they use and the networks they use to communicate. While data is often the most high-profile target, areas of concern also include technology deployed in critical national infrastructure (such as the national grid) or in public safety (such as public transportation)


Robotics continue to automate traditional engineering and maintenance, supplementing or replacing former manual roles. They have also entered the social and domestic space. Robot vacuum cleaners have become a commodity; and Sony has released a new range of its Aibo robotic dogs. Improvements in areas such as voice and facial recognition, speech recognition and synthesis, gestural interfaces, microcontrollers and electro-mechanical components, such as micro servos (and hence the improved fidelity of articulation of robotic limbs) are aiming to make robots more socially acceptable and an increasing part of domestic life, particularly in areas such as assisting the ill or elderly, the disabled, and in entertaining and educating children