Digital twin AIs designed to learn at the edge

Artificial intelligence (AI) startup SWIM is aiming to democratize both AI and digital twin technologies by placing them at the edge without the need for large-scale number-crunching as well as making it affordable.

By Chris Middleton, Vinelake June 10, 2018

With Pure Storage and NVIDIA recently launching their artificial intelligence (AI) supercomputer, it is easy to believe enterprise-grade AI is solely about throwing massive number-crunching ability at Big Data sets and seeing what patterns emerge. But while these technologies notionally are aimed at all types of business, the cost of optimized AI hardware that can be slotted into a data center may be too high for many organizations.

At the other end of the scale are technologies such as IBM’s Watson and Watson Assistant—which can be deployed as cloud services—and of course numerous suite-based AI tools currently offered by many companies. However, for many Internet of Things (IoT) and connected-device deployments, neither data center nor cloud options are realistic, which is why many AI systems are moving elsewhere, fast.

For time-critical processing—such as when an autonomous vehicle needs to avoid a collision—the edge environment and the distributed core are where the real number crunching needs to take place. This is why companies such as Microsoft and Dell have announced new IoT strategies focused principally on the edge and/or the distributed core. The ability to add AI at the edge is an increasingly important element in the IoT, avoiding the need to transfer large amounts of data to supercomputers or the cloud and back again to IoT networks.

Startup SWIM.AI aims to "turn any edge device into a data scientist," without the need for Big Data sets and the enterprise-grade number crunching that goes with them. 

Twin solutions

The company’s AI edge product, EDX, is designed to autonomously build digital twins directly from streaming data in the edge environment. The system is built for the emerging IoT world in which real-world devices are not just interconnected, but also offer digital representations of themselves, which can be automatically created from, and continually updated by, data from their real-world siblings.

Digital twins are digital representations of a real-world object, entity, or system, and are created either purely in data or as 3-D representations of their physical counterparts. For example, every component of the largest machine in history, the Large Hadron Collider, is stored as a digital twin in CERN’s enterprise asset management (EAM) system. This allows scientists to not only know where everything is and what it looks like, but also how well components are performing and when they need upgrade, repair, or replacement.

For most organizations, however, that kind of massive, bespoke program isn’t an option. They need something simpler, easier to deploy, and cheaper. 

Predictive twins

SWIM’s EDX system is designed to enable digital twins to analyze, learn, and predict their future states from their own real-world data. In this way, systems can use their own behavior to train accurate behavioral models via deep neural networks. The important difference to other AI solutions is this ability is offered as a service in real-time, without centralized, batch-oriented big-data analysis. SWIM EDX applications include smart cities, industrial automation, utilities, and information technology (IT) infrastructure optimization. 

Twin management

Gartner views digital twins as one of the top strategic enterprise trends in 2018. However, a key challenge is how enterprises can implement the technology, given their investments in legacy assets.

SWIM believes limited skill sets in streaming analytics, coupled with an often poor understanding of the assets that generate data within complex IoT systems, make deploying digital twins too complex for some. Meanwhile, the prohibitive cost of some digital twin infrastructures puts other organizations off.

"Digital twins need to be created based on detailed understanding of how the assets they represent perform, and they need to be paired with their real-world siblings to be useful to stakeholders on the front line," said SWIM in a statement. "Who will operate and manage digital twins? Where will the supporting infrastructure run? How can digital twins be married with enterprise resource planning (ERP) and other applications, and how can the technology be made useful for agile business decisions?"

The company claims SWIM EDX addresses these challenges by enabling any organization with lots of data to create digital twins that learn from the real world continuously, and to do so easily, affordably, and automatically.

Chris Middleton is the editor of Internet of Business (IoB), a CFE Media content partner. This article originally appeared here. Edited by Chris Vavra, production editor, CFE Media,


Keywords: edge computing, AI

The ability to add artificial intelligence (AI) at the edge is an increasingly important element for the Internet of Things (IoT) as companies look to improve data processing and efficiency. Digital twins are digital representations of a real-world object, entity, or system, and can be enhanced with AI and the IoT. Go Online Read this article at for more information about edge computing and AI applications. Consider this What other improvements can be made to digital twin technology to help AI and the IoT?

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