John Hopfield and Geoffrey Hinton were awarded the 2024 Nobel Prize in Physics for their groundbreaking work with artificial neural networks
The Nobel Prize for Physics has been awarded to John Hopfield and Geoffrey Hinton for their work developing methods that are the foundation of today’s machine learning.
The announcement was made Tuesday, highlighting that this year’s prize was about “machines that learn.”
This year’s winners embody that and were awarded the 2024 Nobel Prize for their foundational discoveries and inventions that enable machine learning with artificial neural networks.
“The laureates’ work has already been of the greatest benefit,” said Ellen Moons, chair of the Nobel Committee for Physics. “In physics, we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties.”
Hopfield, of Princeton University, developed an associative memory capable of storing and reconstructing images and various data patterns.
“The Hopfield network utilizes physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with.”
Hinton, often referred to as the Godfather of AI, hails from the University of Toronto and developed a method that autonomously discovers patterns in data, enabling tasks like identifying specific elements in images,
Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzman machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.
Hopfield and Hinton will equally share the $1 million prize.
Watch the full announcement:
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