NEWSBYTE Edge intelligence startup SWIM.AI has announced new smart city and Internet of Things (IoT) offerings, powered by its EDX AI software.
Earlier this year, the 2015-founded company came out of stealth mode to launch its EDX platform. The product is designed to supply business insights in real time from IoT devices, by autonomously building digital twins from streaming data in the edge environment, via real-time analytics and predictive machine learning.
SWIM EDX runs on existing edge devices, forming a computing mesh for edge processing. In this way, companies can – implicitly – swim in the data lake created by 20 billion connected devices, rather than drown in reams of redundant data.
Smart cities and energy
SWIM has now added dedicated EDX products for smart cities and the IoT, and for smart grids in the utilities, oil and gas sectors.
The company claims that SWIM EDX for Smart Cities integrates with existing urban infrastructures to gather and interpret local data. The product can combine city, traffic, vehicle, pedestrian, parking, and sensor data to power real-time smart city applications.
SWIM also allows cities to offer open data APIs to third parties, simplifying the process of creating new applications.
Meanwhile in the energy sector, SWIM EDX can now help to optimise large-scale utilities, and urban/commercial energy, oil, and gas operations, through smarter edge data, operations, grids, and consumption, according to an announcement from the company.
As before, the software runs locally, analysing data from sensors, meters, energy distribution, upstream/downstream equipment, and utility assets, while again simplifying operations, services, and app development.
SWIM’s EDX platform is now ready for third-party development on the Itron IoT Edge Router, added the company.
Internet of Business says
With Pure Storage and NVIDIA recently launching their AIRI ‘AI supercomputer in a box’ – which is today available in a new Mini version – it’s easy to believe that enterprise-grade AI is solely about throwing massive number-crunching ability at big data sets and seeing what patterns emerge.
But while these technologies are notionally aimed at all types of business, the cost of optimised AI hardware that can be slotted into a data centre may be too high for many organisations.
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, the numerous suite-based AI tools that are currently offered by Oracle, SAP, Salesforce, Google, Microsoft, and others.
But for many IoT and connected-device deployments, neither data centre not cloud options are realistic, which is why many AI systems are moving elsewhere, fast.
For time-critical processing in the IoT – 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 multibillion-dollar IoT strategies that focus 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.
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