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Artificial intelligence increasingly accessible

Achievements of AI-SPRINT European project

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Preparing for the massive use of artificial intelligence, bridging infrastructural and cultural gaps and improving the design and operation of AI applications in IT. These are the goals achieved by AI-SPRINT - Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum, a recently concluded Horizon 2020 project, led by the Politecnico di Milano and funded by the European Union.

Over the course of three years the project aimed to democratise access to AI technologies, by simplifying and accelerating the development of artificial intelligence applications through edge computing. The tools developed during the project were validated by three industrial case studies in personalised healthcare, agriculture 4.0 and maintenance and inspection.

The case studies showed the potential and effectiveness of solutions that can be developed with the AI-SPRINT Studio platform made available on the marketplace AI-on-Demand.

In the area of personalised healthcare, a system was developed during the project to connect wearable devices (such as smartwatches) directly with medical specialists, who, thanks to an analysis of the patient's parameters via AI, can be alerted at the very moment that cardiac abnormalities occur. This experimentation will lead to the launch of a start-up.

As far as agriculture 4.0 is concerned, experimentation has led to the development of a monitoring system on agricultural machinery that uses video cameras to enable the effective distribution of plant protection products in real time with a consequent reduction in chemical pollution.

In maintenance and inspection activities, on the other hand, artificial intelligence has been used to carry out immediate reconnaissance of technical problems in wind power plants, thus reducing the need to send out teams of technicians and lowering maintenance costs.

The AI-SPRINT Studio design environment, developed during the project, allows programmers to balance application performance (such as latency and throughput) and energy efficiency, with the accuracy of AI models, within a secure and private environment.

Digital @polimi