Everyone (or almost everyone) knows about Matrix, the invisible network of data we are immersed in every second of our lives. Everything is intelligent, everything talks and everything communicates, whether it be our car or the coffee maker. In this interconnected system, data and information are the fodder of artificial intelligence which uses them to build behaviour patterns and recognise cause-effect relationships. When certain conditions come about, a certain response is triggered.
Applied for years in logistics to optimise driving routes, this model has gained a foothold in hundreds of other sectors (from fast fashion to tourism) and is rapidly conquering the factory floor, dramatically changing operations.
This constant exchange of information is energy-intensive and tricky to manage. As long as the communicating objects exchange information once a month, like a coffee machine talking to the capsule supplier, the impact is minimum. But in an industrial environment where machines talk to each other 24/7 and 365 days a year, it is a different story: the network band is never sufficient, energy requirements burgeon and the cloud starts to be overcrowded with an enormous quantity of data, much of which is redundant or of little value.
The response to this challenge is Edge AI: it moves artificial intelligence directly onto “the field” and inside the data-generating devices themselves, like PLCs, controllers and smart sensors. All this does away with the intermediate cloud stage.
This paradigm shift produces substantial benefits. It closes the gap between raw data and analysis, and lag times are reduced. Systems respond in real time because they are no longer reliant on an external connection. In critical applications (like visual quality control or line inspections), this means automating decisions at a speed that the cloud simply cannot deliver.
And then there is the question of reliability. On the production floor, where continuity is vital, working with local intelligence avoids downtime even when there is no connection. The machine continues to work because it is “thinking on its own.”
And no less vital is the question of data security: when information is processed locally, the risks inherent in transfer, like loss or breaches, are mitigated. Company know-how and industrial property are better protected.
But the real quantum leap resides elsewhere: Edge AI selects and processes the data before it leaves the machine, sending only truly useful data to the central systems. This results in less network traffic, less energy consumption, leaner data centres and swifter decisions.
There are already a great many interesting applications. In predictive maintenance, machine-learning algorithms installed on controllers provide advance notification of feeble signs of failure and this reduces downtime. In quality control, edge analysis detects defects on items being handled without any need to send images to the cloud. In automatic inspections, an immediate response boosts efficiency and improves accuracy.
In short, Edge AI is not only a technological evolution, but a new industrial philosophy. Data is enhanced at source and this does away with waste and raises performance levels. A properly distributed intelligence which is not up there “in the clouds”, but down on earth where it is actually needed – on the factory floor.
