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RTS | How to cohabit with ChatGPT? [In French]
What can ChatGPT do for us on a daily basis? "To summarize information automatically, to generate texts, email drafts, to produce computer code.
More blog posts
December 12, 2024
SDSC News - December 2024 Newsletter
SDSC News - December 2024 Newsletter
Dear SDSC Community, we are excited to introduce SDSC News, a platform to keep you informed about the latest developments, projects,...
SDSC Newsletters
November 22, 2024
The SDSC Establishes Permanent Presence at Biopôle with Support from Canton Vaud
The SDSC Establishes Permanent Presence at Biopôle with Support from Canton Vaud
Press Release: The Swiss Data Science Center Establishes a Permanent Presence at Biopôle with Support from the Canton of Vaud.
Our News
November 5, 2024
Insights from the "ORD for the Sciences" Hackathon
Insights from the "ORD for the Sciences" Hackathon
Discover the highlights from the ORD for the Sciences Hackathon that took place Oct. 24-25, 2025 at EPFL.
Our News
More news
June 13, 2024
Event wrap-up | SDSC-CONNECT: AI for Precision Medicine
Event wrap-up | SDSC-CONNECT: AI for Precision Medicine
The Swiss Data Science Center held its flagship conference SDSC-Connect, focusing on AI for Precision Medicine, at EPFL’s RolexLearning Center, on 30 May 2024.
Our News
June 23, 2024
EPFL Dimensions | Interview of Dr. Silvia Quarteroni on the risks and opportunities associated with Generative AI
EPFL Dimensions | Interview of Dr. Silvia Quarteroni on the risks and opportunities associated with Generative AI
Dr. Silvia Quarteroni, Chief Transformation Officer and Head of Innovation at the Swiss Data Science Center, provides insights on how AI-driven technology will become part of our daily lives.
Articles about the SDSC
October 31, 2023
Street2Vec | Self-supervised learning unveils change in urban housing from street-level images
Street2Vec | Self-supervised learning unveils change in urban housing from street-level images
It is difficult to effectively monitor and track progress in urban housing. We attempt to overcome these limitations by utilizing self-supervised learning with over 15 million street-level images taken between 2008 and 2021 to measure change in London.
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