St Germain en Laye, October 10th 2024
It takes time in science to estimate the scope of important work. Finally, John Hopfield born in 1933 and Goeffrey Hinton in 1947, have just jointly obtained the 2024 Nobel Prize in Physics for their work carried out since the 80’s on machine learning.
Goeffrey Hinton is known for his applications:
Backpropagation in machine learning is a gradient estimation method commonly used for training neural networks to compute the network parameter updates.
Boltzmann machine, a statistical physics technique applied in the context of cognitive science.
Deep learning (considered as one of the godfathers) is a subset of machine learning methods based on neural networks with representation learning.
Capsule neural network (CapsNet) is a machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization.
John Hopfield developed :
Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory.
Modern Hopfield networks (also known as Dense Associative Memories) are generalizations of the classical Hopfield networks that break the linear scaling relationship between the number of input features and the number of stored memories.
And also Hopfield dielectric, Polariton and Kinetic proofreading
See Nexyad Artificial Intelligence section: Artificial Intelligence: We bring Solutions to your Problems (nexyad.net)
