Nobel price related to statistics

This year, two of the Nobel Prizes were awarded to researchers with clear connections to machine learning research. The Physics Prize went to Hopfield and Hinton for their work on so-called deep neural networks, and the Chemistry Prize went, among others, to Hassabis and Jumper for their development of AlphaFold, a deep neural network for predicting the three-dimensional structure based on a protein's amino acid sequence, a problem that has puzzled scientists for several decades.

Both of these prizes have a clear connection to the field of machine learning, which can be seen as a combination of statistics, applied mathematics, and computer science. In machine learning, predictions are central—it can involve predicting the price of an apartment based on its size and location, predicting whether a picture contains a cat or a dog, or predicting three-dimensional structures based on amino acid sequences. To train these models, or estimate them as is commonly said in statistics, large amounts of data are used so that the model can 'learn' to make accurate predictions. Hinton has long worked with a certain type of model called deep neural networks, which work very well for very complex predictions. It is also these deep networks that are behind AlphaFold.