From IoT to Physical AI: The Intelligence Loop Between Software and the Physical World
A sensor can tell you when your machine is heating up more than normal. A dashboard can tell you that its frequency of vibration is increasing. A machine learning model can estimate that the pattern fits into an abnormal range. But how does the process continue after that? The main challenge in the software system design for industrial applications is rarely data sampling or model training, but…
Industrial applications aim to connect physical objects to digital software systems, providing continuous telemetry streams about location, temperature, vibration, pressure, hours of operation, duty cycle, power consumption, and environmental conditions. While IoT solves the problem of visibility into the telemetry of a physical asset, the main challenge lies in designing the feedback loop between software intelligence and the physical environment.
This architectural progression can be framed as IoT - AI - Physical AI (Loops intelligence back to action).
AIoT (Artificial Intelligence of Things) analyzes raw telemetry to reason about relationships between various signals and identify patterns that suggest potential issues or anomalies. By comparing these patterns to known failure modes, an AIoT application can predict anomalies and provide insights for operational decision-making.
Physical AI takes this concept further by building upon AIoT to make operational decisions that directly impact the physical world. It not only reasons about patterns but also takes action based on these insights, such as identifying under-utilized equipment, maintenance issues, and optimizing workflows in an automated logistics warehouse. Physical AI systems must process telemetry from the physical environment and reason about time series data as well as the mechanics of the physical world they are changing.
The industrial AI software stack includes various layers, such as data gathering through IoT-connected assets, sensor fusion pipelines, data fusion layers, decision engines, automated workflows, and hardware-software interactions. However, one misconception is that more data always leads to better models. In reality, ingesting additional signals can create challenges in terms of storage and processing power without providing substantial modeling advantages.
For physical systems, the context of the data and the reliability of the sensor pipeline are often more important than the complexity of the model.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
