The two winners of this year’s Nobel Prize in Physics used tools from physics to develop methods that form the basis of today’s powerful machine learning. John Hopfield created associative memory capable of storing and reconstructing images and other types of patterns in data. Geoffrey Hinton invented a method that could autonomously find properties in data and therefore perform tasks such as identifying specific elements in images.
“The work of the winners has already been very useful. In physics, we use artificial neural networks in a wide range of areas, for example in the development of new materials with specific properties,” explains Ellen Moons, chair of the Nobel Committee for Physics. Hopfield and Hinton used tools from physics to create methods that helped lay the foundation for today’s powerful machine learning.
John Hopfield created a structure capable of storing and reconstructing information. Geoffrey Hinton invented a method that could independently discover properties of data and which has become important for the large artificial neural networks used today. They both used physics to find patterns in information.
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