Reacción a "Nobel Prize in Physics for Hinton and Hopfield for discovering the basis of machine learning with artificial neural networks"

The first half of the 1980s saw a revival of artificial intelligence (AI), after the so-called 'AI reversal' of the 1970s, thanks to important developments in the field of artificial neural networks led by John Hopfiled and Geoffrey E. Hinton. Hinton.

Hopfiled in 1982 connected the biological aspects of the nervous system with the computational domain. In his paper entitled Neural networks and physical systems with emergent collective computational abilities (PNAS, 1982) he stresses: 'Computational properties useful for biological organisms or for building computers can emerge as collective properties of systems - which have a large number of simple equivalent simple components (or neurons). The physical meaning of addressable content memory is described by an appropriate phase space flow of the state of a system'. With his proposal he defines the so-called 'Hopfield networks', which are a type of recurrent artificial neural network, used as associative memory systems with binary units, which converge in their learning process, and which have applications in different fields such as image processing, speech processing, among others.

Geoffrey E. Hinton was the father of the training and learning model of multi-layer neural models (the one-layer model was the so-called perceptron of the 1970s) called backpropagation. It is a supervised learning method that adjusts the weights of connections in a neural network to minimize the error between the actual output and the desired output. Backpropagation has been crucial for the development of deep learning, because it allows to train deep neural networks efficiently, adjusting weights systematically to minimize error, helps neural networks to learn internal representations of data, which improves their ability to generalize to new data, and opened the door to developments in deep learning, image processing, voice, text... It is the basis of what is today the hatching of generative AI.

These results of the first half of the 1980s laid the foundation stones for the development of the following 40 years that have led to the current emergence of artificial intelligence and deep learning, which is based on these results that sought to emulate the functioning of the human neural system.