CADA tracker · source extraction
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In healthcare, those advancements should improve the accuracy of clinical decisions
and transform the pharmaceutical sector. In the automotive sector, they should support
the development, testing and deployment of innovative software platforms
contributing to the Union industrial leadership in software defined vehicles and
autonomous driving. The Cloud and AI Leadership Initiatives should also reduce
obstacles to test and deploy AI models, in particular within cities and regions
contributing to the development of Union leadership in software defined vehicles and
autonomous driving. Furthermore, Member States should facilitate the development,
testing and deployment of AI systems for autonomous driving, including through
cooperation with the Centres for AI, the automotive industry, suppliers, cities and
regions, with a view to enabling the safe and trustworthy deployment of AI-enabled
connected and autonomous mobility solutions across diverse European environments.
In manufacturing, the Commission should facilitate data pooling across industrial
sectors through trusted third parties to train specialised AI models, ensuring a
sufficient volume of training data, while strictly preserving intellectual property rights.
Secure and verifiable compute approaches should be explored to enable the use of AI
in sensitive contexts. In the defence sector, where AI has emerged as a disruptive
technology with significant impact on security and defence, the Cloud and AI
Leadership Initiatives could support the development of advanced capabilities in full
complementarity with, and without prejudice to, dedicated Union instruments in
support of the defence industry, including the European defence fund (‘EDF’) and the
European defence industry programme (‘EDIP’). In the space sector, digital advances
should transform the way space assets, services and data flows are operated and used,
and in the field of transport, digital advancements in aviation should transform the way
operations are managed, with AI harnessing decades of available mission, operational
and observation data. In the field of climate and environment, geospatial AI should be
developed, in particular by leveraging Earth observation data from the Copernicus
programme and the capabilities of the Union Space and connectivity programmes in
general (15). In agriculture, AI can turn data from sensors, satellites and farm
machinery into actionable insights for farmers. It can strengthen competitiveness and
resilience, for instance by improving yield forecasting, enabling early pest and disease
detection, optimising irrigation and fertiliser use, and supporting more sustainable
food production. As the deployment of AI in industrial contexts requires rigorous
validation in real-world environments, the Union should provide industrial actors with
cloud-based AI tools and testing environments.