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Content focus areas

Many application areas are running into the same AI challenges. These cross-sector issues are important to address, especially when they serve a common interest and individual parties are unable to make progress in them.
Especially for these issues, there are great interests for the Netherlands such as preservation of national autonomy, which cannot be solved without government involvement.

Embedded AI

Research and development of embedded AI provides suitable hardware and applicable software that are essential for realizing autonomous systems, such as in cars and robotics. The AiNed Programme ensures that the Netherlands' current excellent position in embedded systems can also be maintained in the longer term. And increases the opportunities and resilience of the Dutch technical industry with strong participation from SMEs.

Hybrid AI systems

Hybrid AI systems are set up as learning systems for collaborations between humans and an AI-based system. They make decisions that must be well explainable, so that they undesirably affect the daily actions of humans. Few AI solutions exist yet that adequately address explainability of decisions and can comply with future laws and regulations.

AI-controlled and AI-managed infrastructures

With increasing complexity, the safe control and management of vital infrastructures is increasingly using data and AI technology. Such as in power grids, water management, railroads, traffic systems and the Internet itself. Failure or downtime can have serious consequences with a chance of social disorder. The Netherlands still lacks the right AI knowledge and action perspective for predictive maintenance at the system level.

AI for the Dutch language

Despite the fact that there have been major developments in language and speech technology, solutions for dialects, slang, street language and other variants of standard Dutch hardly exist yet. It is a niche market and the legal and ethical frameworks for the use of language and speech recognition are still fiercely evolving. Collaboration between chain parties from different sectors points the way to solutions that work and are acceptable.

Personalisation and privacy protection

Personalisation by tailoring a service, product or process to it is often at odds with privacy and personal data protection. Merely setting frameworks can backfire on innovation and the socioeconomic added value of AI. And technological solutions must be workable within set frameworks. However, designing workable AI solutions that protect privacy is still in its infancy, even internationally.

Sharing data

Many developers of AI applications run into bottlenecks in accessing data. Such as where and how are relevant data stored? What are the (legal) conditions for (responsible) use of the data? And how can the owner or user of data develop a sustainable business case within the framework of legislation (e.g. AVG)? Precisely because there are so many questions, data sharing is a focus area within the AiNed Programme.