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Aaron J. Snoswell, Queensland University of Technology
The 2024 Nobel Prize in Physics has been awarded to scientists John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks”.
Inspired by ideas from physics and biology, Hopfield and Hinton developed computer systems that can memorise and learn from patterns in data. Despite never directly collaborating, they built on each other’s work to develop the foundations of the current boom in machine learning and artificial intelligence (AI).
What are neural networks? (And what do they have to do with physics?)
Artificial neural networks are behind much of the AI technology we use today.
In the same way your brain has neuronal cells linked by synapses, artificial neural networks have digital neurons connected in various configurations. Each individual neuron doesn’t do much. Instead, the magic lies in the pattern and strength of the connections between them.
Neurons in an artificial neural network are “activated” by input signals. These activations cascade from one neuron to the next in ways that can transform and process the input information. As a result, the network can carry out computational tasks such as classification, prediction and making decisions.
Most of the history of machine learning has been about finding ever more sophisticated ways to form and update these connections between artificial neurons.
While the foundational idea of linking together systems of nodes to store and process information came from biology, the mathematics used to form and update these links came from physics.
Networks that can remember
John Hopfield (born 1933) is a US theoretical physicist who made important contributions over his career in the field of biological physics. However, the Nobel Physics prize was for his work developing Hopfield networks in 1982.
Hopfield networks were one of the earliest kinds of artificial neural networks. Inspired by principles from neurobiology and molecular physics, these systems demonstrated for the first time how a computer could use a “network” of nodes to remember and recall information.
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