Hopfield and Hinton, fathers of neural networks and artificial intelligence

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The 2024 Nobel Prize in Physics goes to American scientist John Hopfield and Canadian Geoffrey Hinton for their discoveries that paved the way for the creation of neural networks and laid the foundations of machine learning and artificial intelligence. A sign of the times because they rewarded one of the technologies that is the basis of artificial intelligence.

“This year’s two Nobel Prize winners in physics used the tools of physics to develop methods that support today’s powerful machine learning,” the Nobel Committee said in a press release. Hopfield conducts his research at Princeton University, while Hinton works at the University of Toronto.

Artificial neural networks are machine learning models inspired by the workings of the human brain. They are made up of interconnected nodes, similar to biological neurons, organized in layers. Each layer processes data and passes the information to the next layer, allowing the network to learn complex patterns from the data provided. Neural networks are used to solve problems such as image recognition, speech recognition, machine translation, and many other areas of artificial intelligence. It is the foundation of software's ability to learn and contributed to the birth of transformers that are the basic architecture of ChatGpt and what is known as generative artificial intelligence.

Physicist and biologist John Hopfield contributed his studies to the development of Hopfield's neural network model in the 1980s, which had a fundamental impact on the field of artificial intelligence and machine learning. This model showed how neural networks can store and retrieve information, similar to the brain's associative memory. He essentially created an associative memory capable of storing and reconstructing images and other types of patterns in data.

Geoffrey Hinton has been called the “Godfather of Deep Learning.” He is one of the developers of the backpropagation algorithm, a fundamental method for training neural networks that has enabled major advances in machine learning. Hinton also worked on learning word embedding, a key technique for improving machines' understanding of natural language.