2 minutes reading time (453 words)

Digital twins and digital doubles: populating our virtual worlds

Digital twins and digital doubles: populating our virtual worlds Liebslakritze, CC licence https://www.flickr.com/photos/8069051@N06/15945845318

Years ago when I worked in the semiconductor crystal growth industry, we had a frequent problem with crystal defects that occurred, seemingly at random, during a multi-day growth process. These defects caused total failure of the greater than $80,000 single crystal. One of my proposed solutions was to create a dynamic computer model of the whole system. The simulation would run simultaneously with the real system's control settings and model in real-time the various potentials for defects well in advance of their occurring. The closed-loop SCADA system would then make appropriate corrections to avoid defects and yield a perfect crystal. While theoretically possible, my proposal was unfortunately ahead of its time in terms of the compute power and sensors required vs. those available in the early 2000's.

Fast forward to today, and this concept is called a 'digital twin,' defined as "a virtual model that is essentially the intelligence counterpart to an actual, physical object." Now digital twins are becoming relatively common - "Gartner predicts that by 2021, half of large industrial companies will use digital twins, resulting in those organizations gaining a 10% improvement in effectiveness."

Digital twins rely on three main components: data (from sensors in the Internet of Things, IoT); a 'digital thread' that relays information to the digital twin; and computation (from artificial intelligence, AI). Fortunately, all three are now up to snuff enough to enable useful applications of digital twins. For example, in the oil and gas industry, predictive maintenance via digital twins offers tremendous benefits:

"The digital twin is a system of systems based on a virtual digital copy of all the infrastructure assets as represented by Deep Learning Neural Networks (DNNs). DNNs are a Machine Learning technique that brings us a new paradigm of 'software that writes software' and acts as a compiler for your data to deliver the desired outcome…There is no doubt that smarter, faster, AI-powered applications and digital twins can have a fundamental effect on predictive maintenance in the oil & gas industry, but it will also pave the way for AI and GPU infrastructure enabled edge-to-cloud capabilities to enable full-stream outcomes for production optimization, automated drilling and even bridging the silos across all the operational streams from oil exploration and extraction to processing and distribution."

While industrial systems may be relatively straightforward to simulate, could we advance this concept dramatically to simulate a living human, thereby creating a 'digital double'? What benefits and applications might be created if we have a virtual simulacrum of ourselves with all our knowledge, health status, personality quirks, and worldviews? With faster compute, increasing volumes of data, the creation of multi-purpose platform apps, and artificial intelligence (AI), we are getting close to realizing digital doubles. The implications could be profound.

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