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Devices are evolving. Increasingly devices are being equipped with sensors and attached to the internet. This interconnection of devices with the internet is being called the Internet of Things (IoT).
Data collected by sensors on IoT devices is typically sent back to a collection point, often located somewhere in the cloud. But now, rather than transmitting large amounts of raw data back to powerful cloud servers, devices are incorporating AI on-board to help them locally process and interpret data and only then transmit smaller packets of more consolidated data and information back to a master server.
In this scenario AI is used first locally by the IoT device and then additional AI algorithms can be applied later in the cloud by much more powerful computers with data collected from armies of IoT devices.
Because the processing capabilities of IoT and Cloud devices is vastly different, the AI solutions used by IoT devices and the Cloud are also very different. Edge devices are typically small devices and severely limited in available computing resources. On the other hand, the opposite is true for the cloud, where resources can be scaled to accommodate very large data processing.
Steve Poulsen, President of Signetik, noted this trend and identifies two types of AI:
Small AI – Targets IoT resource-constrained devices where data is processed locally and consolidated.
Big AI – Will use superior resources to quickly and deeply analyze the filtered data for significant trends and patterns.
The combination of Small and Big AI provide a powerful analytic and decision capabilities.
Hanno Schoklitsch, CEO and founder at Kaiserwetter Energy Asset Management, said that “connecting IoT with AI means that there are basically no limits to the amount of real-time and trend data we can integrate and analyze.”