Four Foundational Technology Trends to Watch in 2023

In recent years, the increased connectivity capabilities of massive data aggregation devices have led to a growth in the investment and adoption of emerging technologies and new applications. Advances in artificial intelligence (AI), automation, robotics, and computing, and their intersection has become a focus for many organizations, agencies, and companies across a multitude of industries.

According to recent studies, nearly 77% of devices today use AI technology in one form or another, 91.5% of leading businesses invest in AI, and more than 50% of all enterprises plan to spend more on AI and machine learning in 2023. Increased investments in AI and related technologies will push forward the work on important aspects of AI such as Adaptive AI, Responsible and Xplainable AI, as well as the overall trust in AI and the ethics of its usage.

As more entities adopt AI and related applications, other emerging technologies are entering the playing field. Robotics and robots used for automation are seen more frequently as organizations work to automate their processes to increase efficiency and enhance outcomes. Further, advances in edge computing and more recently quantum computing are seen being made and utilized across industries. Such technologies are being adopted in tandem at increasing rates as they give organizations the ability to adapt to rapidly changing conditions, which is critical to success. Lastly, the metaverse is another technology trend capturing public attention, a collective space that converges many diverse technologies into new applications.

However, the growth of these emerging technologies and new applications also comes with the need to address certain core issues, most notably privacy, security, and ethical concerns.

The four trends that IEEE Standards Association (IEEE SA) expects to see in 2023 in foundational technologies pertain to data governance and AI, automation using robots, edge computing, and the metaverse.

The widespread adoption of AI and machine learning (ML) brings increased focus on responsibility, including data governance and privacy of software-based systems. From the perspective of building customized systems, trust, and interoperability are key. There are multiple elements to trust, consisting of identity, privacy, safety, and security (TIPPSS), underpinned by the focused work of the IEEE SA Foundational Technologies Practice.

The original design of most digital technologies did not anticipate the use of those technologies by children, but today it is of increasing concern, study, and action. At the global level, it has been estimated that already one child in three is an internet user and that one in three internet users is a child under 18 years of age. A survey about social media use found that overall screen use among teens and tweens increased by 17% from 2019 to 2021 during the COVID-19 pandemic—growing more rapidly than in the four years prior.

IEEE SA is at the forefront of the discussion and action with a focus on designing trustworthy digital experiences for children. The standards addressing children’s data governance include IEEE 2089™ Standard for Age-Appropriate Digital Services Framework; IEEE P2089.1™ Standard for Online Age Verification; and IEEE 3527.1™ Standard for Digital Intelligence (DQ).

For children and adults, we are increasingly seeing more attention given to responsible application development, including the integration of ethical and functional requirements. Artificial Intelligence Systems (AIS) enabling many products and services are driven by algorithms invisible to users that still deeply affect their data, identity, and values. Despite the best intentions of a manufacturer, without having a methodology to analyze and test how an end user interprets a product, service, or system, a design process will prioritize the values of its creators. Responsible innovation in the algorithmic era requires a values-oriented methodology that complements traditional systems engineering.

IEEE P7000™ Standards Working Groups and Projects are setting the standards for ethically aligned autonomous and intelligent systems. These efforts are aimed at helping organizations better earn and keep the trust of end users and stakeholders by directly addressing ethical concerns upfront, leading to greater market acceptance of their products, services, or systems. Two examples of note:

• IEEE 7010-2020™ is a standard for assessing the impact of autonomous and intelligent systems on human well-being. By incorporating well-being factors throughout the lifecycle of AI, IEEE 7010 aims to establish metrics affected by intelligent and autonomous systems and provide a baseline for the data that these systems should analyze and include to proactively increase human well-being.
• IEEE 7000-2021™ outlines a Value-Based Engineering (VbE) approach for identifying and analyzing potential ethical issues in a system or software program. The VbE methodology provides a broader lens to consider potential value harms associated with product or systems design. This standard aims to integrate ethical and functional requirements to mitigate risk and increase innovation in systems engineering design and development.

As worldwide regulations ramped up to protect data privacy, new challenges emerged for the use of data and AI, including the need for organizations to adopt federated machine learning systems, which are decentralized. In a centralized machine learning system, data is typically extracted from devices such as laptops and mobile phones and brought to a centralized server where algorithms grab the data, train themselves, and make predictions. However, traditional AI methods (where sensitive user data is sent to these centralized servers) create privacy issues.

Federated machine learning allows multiple parties to collaboratively build and use ML models on distributed and secure data sources while preserving privacy. In response, IEEE SA published IEEE 3652.1™ Standard for Architectural Framework and Application of Federated Machine Learning. This guide serves as a blueprint for organizations to use federated machine learning systems, which harvest data while also meeting privacy, security, and regulatory requirements. To learn more about the standardization of federated machine learning, view a recording of a webinar IEEE SA held on 5 January 2023.

For more than 50 years, robots have been increasingly adopted in the manufacturing sector to improve production efficiencies—the constant evolution of technology also has enabled significant contributions to the development of robotic systems for other uses.

For example, in the healthcare sector robotic systems that can take a patient’s vitals and dispense medicine in an effort to limit healthcare workers’ exposure during the COVID-19 pandemic. We now see medical robots as enablers of “hospitals at home,” whereby robots can play a significant role in taking care of elderly patients with tasks such as dispensing medication, offering video chats with healthcare professionals, and enabling voice commands such as “help me.”

Beyond product manufacturing, robotics in a factory environment has evolved to include material handling and pick-and-place tasks, completing them faster and more efficiently than human labor. These industrial robotic applications can improve high-volume, repeatable processes, such as orienting a part on a conveyor belt and lifting heavy objects. By speeding up the workflow, it is making organizations more profitable, flexible, and responsive.

The most pressing challenges for these robotic uses are the lack of human aspect. Robots do not have the same level of emotion as humans, making it difficult for them to connect with people. Additionally, the cost of robots and maintaining them is expensive. As with many technologies, additional issues include privacy and security.

Unlike systems using physical robots, Robotic Process through Automation (RPA) is a software technology that makes it easy to build, deploy, and manage software robots that emulate human actions interacting with digital systems and software. Rather, the ‘robot’ in robotic process automation is a software robot running on a physical or virtual machine. We are seeing industries increasingly adopt RPA to improve workflow in organizations by having software robots perform simple and repetitive work, thereby freeing up employees to focus on more enjoyable tasks. Data privacy issues linger, however, with concerns over security for communications from machine to machine.

IEEE Standards Association (IEEE SA), in conjunction with the IEEE Robotics & Automation Society (RAS), is at the forefront of standards development that can advance the reusable, scalable, more expedient, and less costly evolution of trustworthy autonomous robots. IEEE 2730™ draft standard, for example, specifies terms, definitions, and classification of medical electrical equipment/systems employing robotic technology (MEERT).

As we continue to discuss emerging technologies such as AI, IoT, and 5G, edge computing dominates trend discussions.

Edge computing has recently become mainstream since the emergence of unstructured data. As it receives vast amounts of attention and industry investment, edge computing is important to connectivity among devices capable of sending and receiving ever-increasing amounts of data. In simple terms, edge computing is the practice of capturing, storing, processing, and analyzing data near the client, where the data is generated, instead of in a centralized data-processing warehouse.

It is predicted that the edge computing market will grow from the already sizeable amount of $44.7 billion in 2022 to $101.3 billion by 2027 at a Compound Annual Growth Rate of 17.8% during the forecast period. This is attributed to the number of applications pushing the adoption of edge computing and recent market acceptance.

In fact, by 2023, it is estimated that more than 50% of new enterprise IT infrastructures will be deployed at the edge. Along with the growth and promising advantages of edge computing is the need to address specific core issues. For example, user identity and privacy face significant challenges as does security, given malware and ransomware threats to IoT deployments.

On the other hand, Edge Computing provides low latency, decentralized and dedicated bandwidth, and is generally more secure. From a performance standpoint, edge computing can deliver much faster response times—locating key processing functions closer to end users significantly reduces latency. In traditional networking, data is typically collected on the edge and transmitted back to centralized servers for processing.

As more applications come online and IoT adoption increases, additional industries will continue to rely on the edge. IEEE SA has a range of standards for edge computing and in the overall space of the edge-fog-cloud continuum, which will enable numerous industry vertical applications, including healthcare, telecom and connectivity, mobility, energy, and more.

The metaverse is the latest technology trend capturing public attention. Metaverse as a concept was coined by author Neal Stephenson in his 1992 sci-fi novel, Snow Crash. He imagines an immersive virtual world where humans live outside of physical reality.

The metaverse will impact the future of our digital world, but when it comes to fruition and what it will include are yet to be agreed upon. What we do know is that virtual technology is a key component, enabling immersive communication experiences through location and context-aware digital services, as well as sensory experiences, such as truly immersive extended reality (XR) and high-fidelity holograms.

The metaverse is made possible through the convergence of many diverse technologies and designing and building these environments comes with many challenges, ranging from technical issues to socio-technical considerations. IEEE SA is at the forefront of developing a sweeping range of metaverse-related standards and resources to address these challenges.

As the metaverse continues to converge diverse technologies and evolve into new applications, we can expect it to profoundly impact our daily life across all industries and sectors, reshaping the economy and society for all humankind.

In 2023 and beyond, we expect that data governance will be a strong focus to ensure a level of consumer confidence, protect users’ privacy, and require transparency and accountability by developers. Furthermore, automation and robotic technology will continue to grow with new, innovative applications to help solve problems and create new opportunities. Lastly, we expect the edge computing market to expand rapidly, bringing significant business benefits and highlighting the need for standardization.

Learn More About IEEE SA’s Work in Foundational Technologies