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This is the approach taken by the Generalitat de Catalunya and SciTech DiploHub
The Government of Catalonia wants to see the region under its administration become a reference point of high-tech innovation and research on a global scale. As part of this ambition, it was decided to launch a section of the ‘Barcelona Alumni’ organization dedicated exclusively to people who work internationally in the field of Artificial Intelligence (AI) and who had completed their studies in Catalonia.
It will be officially presented on 22 December at the annual ‘Barcelona Alumni Global Summit’, which this year will take place online. The overall strategy is based on the concept of ‘scientific diplomacy’ and it sees active cooperation between the regional government, SciTech DiploHub non-profit and local universities, and research centres.
On the administration side, the main stakeholder concerned is the Catalonian Department of Digital Policies, which has a plan to work with a strategy called ‘Catalonia.AI’ – an overall integrated approach aimed at making the region a recognizable name in that sphere.
The AI chapter of the alumni was thought out as a way to tap into what is now an operating pool of talents working in some of the major research centres of the world and bringing them to the cause.
On one hand, their influence and prestige can serve to promote and disseminate the quality of Catalonian science education and attract in its wake future international students to the region further fostering the strength of training there.
On the other hand, in the contemporary world, it is unthinkable that one could make great strides in innovation while staying in isolation. Some of the Catalonian universities alumni today work at places, such as the Singapore Center for Quantum Technologies, the Massachusetts Institute of Technology (MIT), Harvard University and leading companies, such as Google, Apple, IBM, Facebook and Nvidia. Keeping a vibrant link and engagement with these people can only serve to maximize the quality of research performed locally.
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