The Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Ginton "for fundamental discoveries and inventions that enable machine learning using artificial neural networks." The winners were awarded a prize of 11 million Swedish kroner (over a million dollars), which they will share equally.
This was reported by the Nobel Committee.
This yearʼs laureates of the Nobel Prize in Physics are the 76-year-old British scientist and the 91-year-old American scientist Hopfield. They used the tools of physics to develop methods that are the basis of modern powerful machine learning, the press service of the Nobel Committee said.
John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. And Geoffrey Ginton has invented a method that can autonomously find properties in data, thus performing various tasks, such as identifying specific elements in images.
"When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, brain neurons are represented by nodes that have different values. These nodes influence each other through connections that can be likened to synapses and that can be made stronger or weaker. The network learns, in particular, by developing stronger connections between nodes with high simultaneous values. This yearʼs laureates have been doing important work with artificial neural networks since the 1980s," the press release reads.
John Hopfield created a network that uses a method of saving and replaying samples. You can imagine the nodes of this network as pixels. The Hopfield network relies on physical laws that describe the properties of materials due to the atomic spin system, a phenomenon that makes each atom a tiny magnet.
The entire network is described analogously to the energy of a spin system in physics, and the training of the network is to find such a value for the connections between nodes that the stored images have a low energy level.
When a distorted or incomplete image is fed to the Hopfield network, it gradually processes the nodes and updates their values to reduce the energy of the system. So, step by step, the network finds the stored image that is most similar to the one that was presented to it in a distorted form.
Geoffrey Ginton used the Hopfield network as the basis for a new network that uses a different method, the Boltzmann machine. This machine is able to learn to recognize characteristic elements in a certain type of data.
Ginton applied tools from statistical physics, the science that studies systems made up of many similar components. The machine learns from examples that are likely to occur during its operation.
A Boltzmann machine can be used to classify images or create new patterns based on the patterns it was trained on. Ginton continued to develop this technology, which became the impetus for todayʼs rapid development of machine learning.
"The work of the laureates has already brought enormous benefits. In physics, we use artificial neural networks in a wide range of directions, for example, to develop new materials with certain properties," said the head of the Nobel Committee for Physics Ellen Moons.
- The Nobel Prize is one of the most prestigious international prizes awarded annually for outstanding scientific research, revolutionary inventions or a significant contribution to culture or social development.
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