Miscellaneous

• 

  (New) How can AI deliver on its promises in healthcare?

  “It's just completely obvious that in five years deep learning is going to do better than radiologists.” So said AI pioneer Geoffrey Hinton in 2016.

  Anno 2024, we can conclude that no radiologist has become obsolete; there is rather a shortage of radiologists. AI has much to offer healthcare, but most solutions are still in an experimental pre-clinical stage.

  UvA researchers Hoel Kervadec, Evangelos Kanoulas and Somaya Ben Allouch shine their light on how the gap between science and medical practice can be closed.

  For more information, see https://lab42.uva.nl/read/stories/how-can-ai-deliver-on-its-promises-in-healthcare or contact Marion Vetter at m.e.j.vetter at uva.nl.
• 

  `Dick de Jongh on Intuitionistic and Provability Logics' published by Springer

  The book entitled `Dick de Jongh on Intuitionistic and Provability Logics', part of the series Outstanding Contributions to Logic, and edited by Nick Bezhanishvili, Rosalie Iemhoff and Fan Yang, has been published by Springer.
  The book which begins with an autobiographic note by Dick de Jongh can be dowloaded from this link.

  For more information, see https://link.springer.com/book/10.1007/978-3-031-47921-2 or contact Dick de Jongh at d.h.j.dejongh at uva.nl.
• 

  Henkjan Honing receives SMPC Lifetime Achievement Award

  Henkjan Honing, Professor of Music Cognition at the University of Amsterdam, has been awarded the prestigious Lifetime Achievement Award by the Society for Music Perception and Cognition (SMPC). The accolade was presented during the SMPC conference in Banff, Canada, on July 27, 2024, in recognition of his pioneering contributions to the field of music perception and cognition.

  For more information, see https://www.uva.nl/en/shared-content/faculteiten/en/faculteit-der-geesteswetenschappen/news/2024/071/henkjan-honing-receives-smpc-lifetime-achievement-award.html or contact Henkjan Honing at h.j.honing at uva.nl.
• 

  New ILLC Blog post: Going beyond a mathematical investigation of bias

  While mathematical investigation is a useful way to better understand why language models, like ChatGPT, may be gender biased, it is also useful to take a step back and to consider bias in NLP from a broader perspective.

  For more information, see https://resources.illc.uva.nl/illc-blog/going-beyond-a-mathematical-investigation-of-bias/ or contact Oskar van der Wal at o.d.vanderwal at uva.nl.