This Call for Solutions is the continuation of the Call for Challenges (24 November 2020 to 4 March 2021) in which 13 Challenge Owners are currently being selected. The Challenge Owners (SMEs, Mid-Caps and large companies) will work together with the 41 Solutions Providers to develop at least two solutions per each of the challenges.

The current list of Challenges is available HERE. This list is being added continuously and will be completed by Mar 2021.

70,000€ for Solution Providers, training, mentoring and more

The 41 European start-ups, SMEs, Mid-Caps, large companies, and research organizations selected to provide AI solutions that graduate from the Support Program will be showcased as new resources on the AI4EU Platform.

They will also get financial support of up to 70,000€ and training on the AI4EU Platform (resources, Acumos, publication), online mentoring every two weeks to track the development of the solution, and visibility through the AI4EU channels, events and ambassadors.

With these open calls, an objective of the AI4EU Platform is to showcase its capacity to bring an entire ecosystem to solve AI challenges in various areas by developing solutions prototypes backed by AI technologies.

The Ongoing Challenges AI4EU Looks For

In addition to the selected Challenges, AI4EU is actively looking for particular problems with an industrial, business or societal impact that can be solved by exploiting the existing AI resources included on the AI4EU Platform or developing new components and have potential to be replicable in the sector. The Call for Challenges will close on 4 Mar 2021.

These challenges have to be challenges based on AI technologies that could help businesses and the public sector to run their business and activities more efficiently or to deliver innovative services to their customers and citizens. AI4EU is looking for solutions that solve one of the following challenges that have been published on the AI4EU Platform:

• AI-based throat-swab robot usable in pandemic prevention.
• Effective management of Personally Identifiable Information (PII) for agrifood data.
• Reinforcement learning based machine tool control.
• Enhancing clinical AI-workflows.
• Modelling of drugs via deep learning neural network.
• Optimize stock management with smart procurement strategy.
• Identification of Colon Cancer risk factors.