News
Swiss Academies of Arts and Sciences a+
International science advisors: step up global action to halt biodiversity decline
Ahead of the UN Biodiversity Conference (COP 15, 7-19 December), senior science advisors call on governments to: commit to the Global Biodiversity Framework; invest immediately in actions of beneficial effects on biodiversity; implement robust and effective mechanisms for biodiversity monitoring; coordinate action at local to international levels; foster innovation through the weaving of biodiversity knowledge from diverse systems; and engage the public. These measures are considered important and necessary because three decades after the 1992 Convention on Biological Diversity (CBD), the global response to biodiversity decline remains woefully insufficient.
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.