Making Sense of Spatial Computing and Digital Twins for Industry

The problem with the phrase “buzzword” is the unspoken implication that the technologies and innovations being labeled as such don’t provide real value, at least not yet.

Digital twins, and the industrial metaverse more broadly, have for years been buzzwords hovering around the industrial economy, a landscape traditionally characterized by heavy machinery, sprawling supply chains and capital-intensive processes.

But as industrial sectors, and other complex and sophisticated areas like medicine, healthcare and even the defense industry, continue their digital renaissance, central to this transformation is the ongoing maturation of two technologies: artificial intelligence (AI)-powered spatial computing and digital twins. Together, they look increasingly poised to redefine how industries design, manage, and optimize physical operations, merging the digital and physical worlds in unprecedented ways.

On Tuesday (Jan. 14), news broke that AI giant Nvidia was investing in MetAI, a Taiwanese startup focused on creating AI-powered digital twin virtual replicas of real-world objects or environments, which can be used in various industries, including manufacturing, retail and entertainment.

On Wednesday (Jan. 15), Lockheed Martin announced it was exploring the concept of a human digital twin, a virtual representation of aircraft pilots, or what the company calls the e-Pilot, designed to assist the human pilot in awareness and provide enhanced aircraft control options during flight safety critical situations — takeoff, landing, air refueling and tactical engagements with threats.

Meanwhile, a Friday (Jan. 17) study published in Nature separately found that the usage of digital twins in precision medicine is an increasingly viable one, propelled by extensive data collection and advancements in AI, alongside traditional biomedical methodologies.

Clearly, digital twin technology is proving ready to move beyond buzzword status.

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The synergy between spatial computing and digital twins could be where true transformation lies. Spatial computing provides the interface and interaction layer, while digital twins supply the data and intelligence.

Spatial computing refers to the use of digital technologies to map, understand and interact with physical spaces. At its core, it encompasses technologies such as augmented reality (AR), virtual reality (VR), mixed reality (MR) and the Internet of Things (IoT). Spatial computing enables devices to perceive and respond to the physical environment in real time, creating immersive and interactive experiences.

As PYMNTS has covered, there’s an underlying assumption held by many observers that one of the bigger themes behind the tech sector’s push toward XR environments is the fact that XR systems rely on real-time spatial mapping to create interactive environments, using advanced sensors like LiDAR, cameras and motion detectors to map physical spaces with precision. For tech companies, XR is more than an immersive medium — it can serve as a data generation engine for AI systems. By leveraging spatial data, XR helps to accelerate the training, deployment and performance of AI in real-world applications. As the XR ecosystem grows, the synergy between AI and XR holds the potential to reshape industries, bridge physical and digital realms and drive innovation.

A standout example of spatial computing is Nvidia’s Omniverse platform, which integrates digital twins with real-time spatial computing to create shared virtual spaces for design and simulation. This approach not only accelerates innovation cycles but also democratizes access to cutting-edge technologies for smaller firms that previously lacked such capabilities.

See also: How the B2B Metaverse Opportunity Enables Tomorrow’s Industrial Economy

One of the most transformative aspects of digital twins lies in their predictive capabilities. By analyzing historical and real-time data, these virtual counterparts can foresee potential issues and recommend solutions. This predictive power extends across industries — from anticipating equipment failures in energy plants to predicting patient outcomes in precision medicine.

Despite their promise, digital twins and spatial computing face hurdles that need addressing. Data privacy and security remain critical concerns, particularly as these technologies generate and process vast amounts of sensitive information. Moreover, the high upfront costs and technical expertise required for implementation can deter smaller organizations from adopting these innovations.

Interoperability is another challenge. For digital twins and spatial computing to reach their full potential, they must integrate seamlessly with existing systems and data sources. This requires open standards and collaborative efforts across industries and technology providers.

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