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Swiss Academy of Engineering Sciences SATW
TecDay a Kanti Baden: 1200 giovani scoprono la tecnologia in prima persona
Programmare robot, produrre musica da rifiuti elettronici o immergersi nelle profondità marine con un biologo marino: Ieri, mercoledì, la scuola cantonale di Baden si è trasformata in un laboratorio di scoperta per circa 1.200 giovani. In occasione dello STEM Day, organizzato in collaborazione con il TecDay dell'Accademia svizzera delle scienze ingegneristiche SATW, gli alunni hanno sperimentato la tecnologia e la scienza in modo pratico, creativo e coinvolgente. Al posto dei libri di testo e dell'insegnamento frontale, il programma prevedeva oltre 60 workshop, tre conferenze stimolanti e incontri con esperti del mondo tecnologico e della ricerca svizzero.
Swiss Academy of Engineering Sciences SATW
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‘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
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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
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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.