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Focus areas

Many different application areas come up against the same AI challenges. It is important to address these cross-sector issues, particularly when they serve a common interest and where individual parties are unable to make progress. It is precisely these topics that encompass major interests for the Netherlands, such as preservation of national autonomy, that cannot be solved without government involvement.

Embedded AI

Research and development of embedded AI provides appropriate hardware and applicable software that is essential for realising autonomous systems, such as in cars and robotics. The AiNed programme is ensuring that the Netherlands’ current strong position in embedded systems can also be retained in the longer term. It is also increasing the resilience of Dutch technical industries and the opportunities available to them, with strong participation from SMEs.

Hybrid AI systems

Hybrid AI systems are designed as self-learning systems for cooperation between humans and an AI-based system. They make decisions that must be easily explainable, so that they do not have an undesirable effect on people’s day-to-day activities. As yet, there are not many AI solutions that can adequately explain decisions and comply with upcoming legislation and regulations.

AI-controlled and AI-managed infrastructures

Increasing complexity means that safe control and management of critical infrastructure items are increasingly being based on data and AI technology. Examples include electricity grids, water management, railways, traffic systems and the internet itself. Failure or relapse can have serious consequences, with the risk of social disorder. The Netherlands still does not have the right AI knowledge and perspectives for taking action for predictive maintenance at the system level.

AI for Dutch language

Despite the major developments in language and speech technology, there are still scarcely any solutions for dialects, jargon, street slang and other variants of standard Dutch. It is a niche market and the legal and ethical frameworks for using language recognition and speech recognition are still in a state of flux. Cooperation between parties in the chain from different sectors will point the way to solutions that are both functional and acceptable.

Personalisation and privacy protection

Personalisation involves tailoring a service, product or process to personal preferences, which often conflicts with privacy and the protection of personal data. Imposing frameworks can of itself be counterproductive for innovation and the socioeconomic added value of AI, yet technological solutions must be viable within the frameworks imposed. However, the design of workable AI solutions that protect privacy is still in its infancy, both here and internationally.

Data sharing

Many AI application developers run up against bottlenecks in access to data, such as where and how relevant data is stored, the legal and other conditions for responsible use of the data, and how the data owner or user can develop a sustainable business case within the legislative framework user (e.g. the GDPR). Precisely because there are so many questions, data sharing is a focal theme within the AiNed programme.