The machine learning victories at the 2024 Nobel Prize Awards and how to explain them

Anna Demming reports on what the prizes were awarded for and how finding connections between the two approaches to machine learning may help towards explaining how "black box" algorithms reach their conclusions. Few saw it coming when on 8th October 2024 the Nobel Committee awarded the 2024 Nobel Prize for Physics to John Hopfield for his Hopfield networks and Geoffrey Hinton for his Boltzmann machines as seminal developments towards machine learning that have statistical physics at the heart of them. The next day machine learning albeit using a different architecture bagged half of the Nobel Prize for Chemistry as well, with the award going to Demis Hassabis and John Jumper for the development of an algorithm that predicts protein folding conformations. The other half of the Chemistry Nobel was awarded to David Baker for successfully building new proteins. While the AI takeover at this year's Nobel announcements for Physics and Chemistry came as surprise to most, there has been some keen interest on how these apparently different approaches to machine learning might actually reduce to the same thing, revealing new ways of extracting some fundamental explainability from the generative AI algorithms that have so far been considered effectively "black boxes".