“The AIoT trend is heralding new possibilities”

Artificial Intelligence of Things: How combining networked sensors and actuators with artificial intelligence is raising new research questions and creating new business models

Dr. Usländer, what does Artificial Intelligence of Things – AIoT for short – refer to?

Thomas Usländer: AIoT is all about merging Internet of Things technologies with methods and tools used in artificial intelligence. This interplay creates huge added value in every IoT domain, especially automation technology and networked industrial production. That’s because the “things” in the IoT, such as machine tools, can use sensors to supply exactly the data that machine learning (ML) methods need and that can be quickly evaluated and efficiently converted into useful information by these very methods. This opens up the scope for new applications and business models – and it raises exciting research questions.

Could you give us some examples?

Usländer: A fascinating area of research is federated learning, in which an ML model is trained on multiple devices. Instead of exchanging all data, parts of the ML model are generated locally and then combined to form one large model – an approach that has particular advantages for data protection.

An exciting concrete innovation topic is the application of ML methods in digital twins of production facilities and their assets, whereby the digital twins are continually synchronized with the states and behavior of the real “things”.

How can companies exploit this potential?

Usländer: Many companies have already invested in digitalizing production processes and the accompanying IoT technologies: for example, to monitor their facilities remotely. The logical next step is to evaluate data using AI solutions that can be implemented in the edge or in the cloud. However, this does mean bringing together a range of areas and disciplines – specifically, production engineering, informatics/IT and data science/AI. As we know from many projects in practice, this is by no means trivial: The use of AI methods is hard to plan and subject to its own laws, which are often alien to engineers and can be difficult to reconcile with the operational culture of established production facilities. However, we have the expertise and the AI systems engineering methods and tools that are needed to provide companies with the best possible support during this transformation journey.


Annual report »We make AI fly«

The above interview is taken from our Annual report 2021/2022, which focuses on Fraunhofer IOSB's AI application expertise.


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