Statement
Swiss Academies of Arts and Sciences a+
ERI Dispatch 2025-2028: Universities and funding organisations for research and innovation fear cutback in services
The Federal Council plans to invest less than before in education, research and innovation (ERI) in the coming years. In its ERI Dispatch 2025–2028, the Federal Council proposes only a slight nominal growth for the ERI sector. Due to the expected inflation, a zero or negative growth is possible in real terms. A cutback in services at the ERI institutions concerned is therefore foreseeable. The universities and research and innovation promotion organisations are therefore calling for significant, real growth for the entire HERI sector over the next four-year period. Only in this way can they continue to make their contribution to progress and prosperity in Switzerland.
Swiss Academy of Engineering Sciences SATW
News
‘The future is by no means set in stone’ – Vice-Chancellor Rachel Salzmann on Switzerland in 2040
What will Switzerland look like in 2040? In its report ‘Perspectives for Switzerland 2040’, the Federal Chancellery outlines three possible futures and derives six areas for political action from them. In an interview with the SATW, Vice-Chancellor Rachel Salzmann explains why the future is by no means set in stone, why she describes the scenarios not as bleak but as challenging, and where Switzerland could take a leading role in technology.Swiss Academy of Engineering Sciences SATW
News
Fair data use starts with asking the right questions
Data helps businesses to better understand their target groups and tailor their offerings more effectively. However, not everything that is technically possible is also responsible. How can SMEs ensure that they use data fairly and transparently? The Data Fairness Label from Swiss Insights offers practical guidance on this.Swiss Academy of Engineering Sciences SATW
News
Machine learning demystified: understanding superhuman performance
Artificial intelligence can outperform humans in certain tasks. But how meaningful are such achievements, and where are the limits? The key factors are the quality of the training data, realistic performance tests and the transparency of the results. What businesses need to know to deploy AI systems reliably.