Future Strategic Initiatives

Complex systems management

A large part of the potential value of AI lies in making operations and processes more effective, such as better resource use in cloud and telecom systems, more efficient supply chains and production, and better diagnostics and resource management in healthcare. Automation of these systems typically rely on both data driven methods for example for anomaly detection, diagnostics, root cause analysis, and demand forecasting, and capacity and resource planning and scheduling.

The intersection of machine learning and operations research has the potential to make many industries and organisations dramatically more efficient, not least medium-sized companies. As this type of application is central in Sweden, both industry and public sector, we are looking at coordinated initiatives to build and share competence, knowledge and platforms.

Privacy preserving solutions

When and how to share data and information is a central question for all companies and government agencies working with data driven methods - one prominent example is healthcare facilities and hospitals. While there might be immense value in data outside of an organization that controls it, dissemination is often very difficult due to privacy concerns (the data might contain e.g. personal information) or concerns about how much that data might divulge about the business and what competitive advantage it provides.

To gain full value of AI and data driven methods in Sweden, we need to find ways to work with sensitive data in a Swedish context. This means not only developing and understanding methods for privacy-preserving data- and model sharing, but also legal aspects and best practises when collaborating and implementing AI solutions. As such, privacy preserving AI is a key area for accelerating AI in Sweden on a systemic scale. 

Multi-modal insights

With increased collaboration around real-world AI applications, the ability to combine and reason over data from different industrial and scientific domains is becoming steadily more important. Similarly, new AI applications are more and more relying on multiple types or modes of data to be able to reach reliable performance. It is often no longer enough to only leverage text or structured data, but a combination of such data sources with e.g. images, video, and knowledge graphs is crucial to extract all necessary information. Furthermore, several critical components of larger AI systems such as reliable speech-to-text exist specifically across several modes of data.

To help develop these components in collaboration and to expand our ability to work together on important applications, multi-modal insights will be one of our strategic areas going forward. Expanding on our work within applied language technologies, we will catalyse high-value applications spanning several data modalities, tackle key research and technical challenges, and develop best practises for collaborative multi-modal applications.