Knowledge representation and reasoning
Whenever we want to make a decision or check something, correct information is crucial. The more clear the information, the better. If not, and for example if different terms are used for the same concept, there will be disruptive ‘noise’. Then you won’t be able to guarantee effective decision support or real-time control. The problem, however, is that many systems offer ambiguous information.
To solve this problem, we use the AI technology 'knowledge representation & reasoning'. This enables us to recordknowledge from the domain so thatthe same terms are used consistently and information is presented clearly. Only then can we reason about this knowledge in the right way. New facts and insights can be derived, which strengthens decision support and real-time control.
OWL formats present unambiguous information
TNO brings visibility to the most important concepts and their interrelationships within a domain. We present these knowledge models using various AI tools in the form of Knowledge Graphs of Ontology Web Language (OWL) formats.
We develop such an ontology in collaboration with domain experts. By presenting relevant information and possible trends in an unambiguous way, a reasoning machine can exploit this ready-to-use data to derive new insights and connections.
Knowledge representation & reasoning is a key technology within AI. It supports decision-making and makes real-time control effective. - Jack Verhoosel - senior business consultant & architect
Hybrid AI is twice as powerful
The AI knowledge representation & reasoning technique can be very valuable in combination with machine learning. In this way, machine learning contributes by taking into account the uncertainty of predictions. And knowledge representation & reasoning, in turn, helps to interpret the internal relationships within a domain.
This combination of reasoning (knowledge representation & reasoning) and learning (machine learning) is called Hybrid AI. In addition to decision support and real-time control, this also helps to find trends and patterns. This technology is currently used in various sectors, such as agriculture, horticulture, defence, industry and energy.
Linda is consultant in de department Data Science. Linda develops semantic standards and advises organizations on how to become interoperable. Her specific focus is on how organizations can develop and manage their semantic standards, if the organization is dynamical and thus needs a combination of expert knowledge and data. Typical customers for Linda are branche or standardization organizations. She works in several domains, the last years with a focus on the labour market with projects such as Skills Matching and SETU.
Christopher BrewsterFunctie:Senior scientist
Christopher Brewster is a Senior Scientist in the Data Science group and Professor of the Application of Emerging Technologies in the Institute of Data Science, Maastricht University. His research has focussed on the application of Semantic Technologies, Open and Linked Data, interoperability architectures and Data Governance, mostly to the food and agriculture domains.
Daniël WormFunctie:Senior consultant
Jok TangFunctie:Deputy Research Manager Data Science
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AI Systems Engineering & Lifecycle Management
The AI system for the future. At TNO, we work on AI systems that remain reliable and can handle new functions in the future.
You can read about how AI is educated in Chapter 1. How can we make clear to AI which goals we want to pursue as humans? Andhow can we ensure intelligent systems will always function in service of society?
Innovation with AI
What does that world look like in concrete terms? Using numerous examples, TNO has created a prognosis for the future in Chapter 2. Regarding construction, for example, in which AI will be used to check the quality, safety, and energy efficiency of buildings before they are actually built. Or healthcare, where robots will partly take over caregivers’ tasks and AI will be able to autonomously develop medicines.
Innovating with innovation AI
How AI will change research itself is explained in Chapter 3. For example, what role will AI be permitted to play in knowledge sharing? And what will happen when we make machines work with insurmountably large data sets?
David Deutsch on the development and application of AI
Peter Werkhoven, chief scientific officer at TNO, joins physicist, Oxford professor, and pioneer in the field of quantum computing, David Deutsch, for a virtual discussion. Deutsch set out his vision in 1997 in the book, The Fabric of Reality. Together, they talk about the significance of quantum computing for the development and application of AI. Will AI ever be able to generate ‘explained knowledge’ or learn about ethics from humans?