Two Scientists win Nobel Prize in Physics for laying groundwork for machine learning

So far, 224 individuals have been awarded the Nobel Prize in Physics
Two Scientists win Nobel Prize in Physics for laying the groundwork for machine learning
The laureates’ discoveries and inventions formed the building blocks of machine learning that can aid humans in making faster and more reliable decisionsPhotograph: Nobel Prize/X
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The 2024 Nobel Prize in Physics was awarded to John J Hopfield and Geoffrey E Hinton for laying the groundwork for machine learning in 1980s which served as a prototype for the present-day advancements in artificial intelligence.

The duo — Hopfield from Princeton University, United States and Hintion from the University of Toronto, Canada — are awarded for “foundational discoveries and inventions that enable machine learning with artificial neural networks.”

Machine learning is a type of artificial intelligence and artificial neural network (ANN) — a machine learning programme designed to mimic the brain. 

The research on machine learning based on ANN began in the 1940s, but became a versatile and powerful tool with widespread applications only over the last three decades.

“Learning is a fascinating ability of the human brain. We can recognise images and speech and associate them with memories and past experiences. Billions of neurons wired together give us unique cognitive abilities,” Ellen Moons, Professor of Physics at Karlstad University, said during the press conference.

ANN, she added, is inspired by a network of neurons in our brains. According to the expert, the ANN has helped advance physics topics such as particle physics, material science and astrophysics while also finding use in daily life, for instance, in facial recognition and language translation.

The laureates’ discoveries and inventions formed the building blocks of machine learning that can aid humans in making faster and more reliable decisions, the expert noted.

ANN, unlike software, can mimic human functions like memory and learning. It is inspired by the brain’s neural network, consisting of neurons with advanced internal machinery. 

Neurons can send signals to each other through the synapses, a specialised connection between two neurons. As humans learn something new, the connections between some neurons get stronger, while others get weaker.

ANN, on the other hand, are built from nodes, which function like neurons and have a certain value attached to them. The nodes are connected and, when the network is trained, the connections between nodes that are active at the same time get stronger, otherwise get weaker. 

The ANN is trained to carry out certain tasks by developing stronger connections between nodes with simultaneously high values, instead of merely executing a predetermined set of instructions.

Hopfield advanced this field by creating an associative memory that can store and reconstruct images and other types of patterns in data. He invented a method called the “Hopfield network, which, when trained, can recreate data that contains noise or which has been partially erased".

Meanwhile, Hinton developed a method that can independently discover properties in data, an important feature for large ANN in use as well as performing tasks such as identifying specific elements in pictures.

So far, 224 individuals have been awarded the Nobel Prize in Physics.

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