Areas of Expertise within Center for Applied AI
Learn more about our areas of expertise within AI and its applications.
An expertise is an area of AI in which RISE conducts research and development with specific and advanced skills. The areas are supported by multi-year research and development projects funded by Sweden, the EU and the industry. More information and contact details for the research groups can be found on the respective expertise page.
AI for Chemistry and Toxicology
AI for Chemistry and Toxicology is based on the competence of several RISE units: Process Chemistry, Toxicology and AI/Digital Systems.
WE OFFER: Unique edge in the combination of computer science, toxicology, chemistry and materials
and projects in chemical processes, toxicology and AI/ML as descriptors, prediction and pattern establishment.
Deep learning
Deep learning has taken the world by storm as the modern day work-horse powering many recent solutions in artificial intelligence. Deep neural networks are used in many applications in natural language processing and image analysis and have replaced older techniques that require more manual work.
Natural Language Processing
Natural Language Processing (NLP) is the task of using computer programs to analyze, understand and generate natural language. Our research concerns both basic algorithms for language AI, but also how such algorithms can be used to solve various practical tasks and problems involving language data.
Computer vision
Most autonomous systems rely on images to understand complex environments and make decisions. As part of the RISE digitalization, we carry out image analysis studies that utilize both traditional and AI methods to solve practical problems in various application areas. Examples include recognition of human behaviour and tracking of traffic objects.
Privacy preserving machine learning
Today, large amounts of data are used to train AI systems that can be of great benefit to humanity. But what the trained systems remember from the training data is not always clear. We work to make it clear what a system remembers, and to create methods that can guarantee that the system remembers what we want.
Engineering Operational AI
The development of operational AI/ML solutions (Artificial Intelligence / Machine Learning) places high demands on both tools and processes. We work with the entire flow in our expertise area, from data to deployed model in an operational environment (fig. 1), to ensure quality and delivery throughout the life cycle.
Industrial Applied AI
Adapting to the new AI-driven paradigm guarantees sustainability, competitiveness and success of the future industries. AI is already changing the entire business models by offering new ways of automation and knowledge creation. RISE will help you to see the possibilities, identify the opportunities and realise your AI strategies.
Federated learning
Federated learning take advantage of data that is distributed across a number of clients. Learning takes place locally on the clients, and with the help of a central server these clients can collaborate and achieve an even better generalization. This provides privacy benefits, as well as distributed compute.
Machine Learning for Uncertainty Propagation (MLUP)
Machine learning (ML) is an efficient tool to mimic complex processes. Numerical simulations, such as computational fluid dynamics (CFD) is often extremely time demanding and may occupy considerable computer resources for an extended period of time resulting in a very limited series of result.
Artificial Intelligence for Batteries
We help you apply Artificial Intelligence and machine learning approaches for battery management, with the potential to enhance performances, lifetime, reliability, and safety of battery systems for mobility and stationary applications which will strengthen the battery-related business for your company.
Driver modelling
A paradigm shift within the transport system is at our doorstep. Transport is becoming commodity and vehicle automation and servification are the enablers. As the role of the driver changes from actively driving, to passively riding along, the measures to understand driver engagement need to be updated.