Artificial Intelligence of Things (AIoT) stems from the convergence of two trends that are dominating the technology industry-the Internet of Things (IoT) and Artificial Intelligence (AI). IoT can be thought of as the digital nervous system, providing massive amounts of real-time data from various sensors and devices, while AI is the brain that makes decisions which control the overall system.
In summary, the first generation of cloud-based IoT provided five key capabilities:
- Collect – Telemetry data from a large number of devices and sensors is collected at a central location.
- Store – The telemetry data is stored in scalable storage systems such as data lakes.
- Process – Big Data platforms are used to process and analyze the telemetry datasets.
- Analyze – The insights from the Big Data systems were utilized to present the analysis through rich visualizations
- Control – Device operators and field engineers control the devices based on the recommendations from Big Data systems.
By combining AI with IoT, we add an important ability to connected systems – Act.
Acting on the patterns and correlations from the telemetry data, AI plugs a critical gap by taking appropriate actions based on the data. Instead of just presenting the facts to humans to enable them to act, AI closes the loop by automatically taking an action. It essentially becomes the brain of the connected systems.
AI is going to supercharge IoT at two different levels. Firstly, it impacts the telemetry data by augmenting the sensors with intelligence. Secondly, AI will be used to analyze the inbound telemetry data stream in real-time or in batch mode. It plugs itself into the start (devices) and the end (analysis) of the IoT spectrum.
For example, a camera that is treated as an image sensor will send every frame to the IoT system to analyze the feed for certain objects. By applying AI to the camera device, it sends the frame only when a specific object is detected. This significantly speeds up the process while saving the CPU from processing every frame. The same principle may be applied to speech synthesis and other forms of telemetry data. AI-enabled sensors are the future of IoT systems. Smart cameras powered by AI accelerators will soon become the standard image sensors.
By applying deep learning models based on neural networks to incoming sensor telemetry data, sophisticated IoT systems will be able to find anomalies in real-time. When a critical error is predicted by the neural network, the faulty device may be shut down to avoid a fatal accident or an event. The key difference between existing rules engine of IoT and AIoT lies in being proactive vs reactive. Current IoT systems are designed to react to an event while AIoT systems can proactively detect failures and events. The infusion of AI in IoT systems delivers the promise of predictive maintenance which will help organizations save millions of dollars in support and maintenance of equipment.
Our platform supports the future of automation that lies in the convergence of AI and IoT. Artificial Intelligence of Things will impact almost every industry vertical including automotive, aviation, finance, healthcare, manufacturing and supply chain.