With world-class research institutes in artificial intelligence (AI), Sweden keeps up with all the latest ideas – and sometimes even steps out ahead. But blue-sky research doesn't always lead to practical solutions that can be used by industry. That's where AI Sweden plays a key role. An important first step in the development of AI Sweden came when Mikael Ljungblom was working as a political advisor to the Swedish minister for digital development. While travelling to see what other countries were doing, Ljungblom and his colleagues saw that countries such as Japan and China were investing in AI.

Sweden's attitude towards innovation is perhaps best exemplified by the Swedish innovation agency, Vinnova, a government agency founded in 2001 based on a series of predecessors going back to at least 1968. The innovation agency functions much like its counterparts in other countries, similarly to the Finnish Funding Agency for Technology and Innovation (Tekes) in neighbouring Finland, and to the part of the US National Science Foundation (NSF) that does seed funding on the other side of the Atlantic. The Swedish government gives Vinnova more than €300m each year to invest through grants to different kinds of actors, which might be small companies, research institutes, large competence centres, or consortia of companies working together on projects. Vinnova invests this money along 10 different themes, including sustainable industry and digital transformation. To report on the social and economic effects of its funding, the agency produces two impact studies annually.

Common to all projects is support från Uppsala University Innovation and success in securing external funding to further enhance development opportunities. Proteins are the workers of the cell, and many proteins interact with each other. In order to understand the importance of these interactions, there is a need to measure both free and interacting proteins. Ola Söderberg, professor at the Department of Pharmaceutical Biosciences, has developed a method to label each protein with its own unique colour, making it possible to measure the proteins individually. At the same time, the proportion of proteins that bind to each other are labelled with a combination of the colours.