CINXE.COM

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class="aside-navigation aside-navigation--mobile input-select js-aside-navigation--mobile" name="Go to laureate page"> <option value="" selected disabled>Navigate to:</option> <option value="https://www.nobelprize.org/prizes/physics/2024/summary/"> Summary</option><optgroup label=" Laureates"><option value="https://www.nobelprize.org/prizes/physics/2024/hopfield/facts/">- John J. Hopfield</option><option value="https://www.nobelprize.org/prizes/physics/2024/hinton/facts/">- Geoffrey Hinton</option></optgroup><option value="https://www.nobelprize.org/prizes/physics/2024/prize-announcement/"> Prize announcement</option><option value="https://www.nobelprize.org/prizes/physics/2024/press-release/" disabled selected> Press release</option><option value="https://www.nobelprize.org/prizes/physics/2024/popular-information/"> Popular information</option><option value="https://www.nobelprize.org/prizes/physics/2024/advanced-information/"> Advanced information</option></select> <h5 class="wp-block-heading has-16-font-size"><b>English</b><br /><a href="https://www.nobelprize.org/uploads/2024/11/press-physicsprize2024-2.pdf" target="_blank" rel="noreferrer noopener">English (pdf)</a><br /><a href="https://www.nobelprize.org/prizes/physics/2024/221989-press-release-swedish/">Swedish</a><br /><a href="https://www.nobelprize.org/uploads/2024/11/press-physicsprize2024-swedish-2.pdf" target="_blank" rel="noreferrer noopener">Swedish (pdf)</a></h5> <figure class="wp-block-image"><img decoding="async" width="190" height="80" src="https://www.nobelprize.org/uploads/2018/06/kva_logo_09-25.gif" alt="Logo" class="wp-image-7288"/></figure> <p>8 October 2024</p> <p><a href="http://www.kva.se/en/" target="_blank" rel="noopener noreferrer">The Royal Swedish Academy of Sciences</a> has decided to award the Nobel Prize in Physics 2024 to</p> <p><strong>John J. Hopfield</strong><br />Princeton University, NJ, USA</p> <p><strong>Geoffrey Hinton</strong><br />University of Toronto, Canada</p> <p><em>“for foundational discoveries and inventions that enable machine learning with artificial neural networks”</em></p> <h2 class="wp-block-heading">They trained artificial neural networks using physics</h2> <p class="ingress">This year’s two Nobel Laureates in Physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures.</p> <p>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, the brain’s neurons are represented by nodes that have different values. These nodes influence each other through connections that can be likened to synapses and which can be made stronger or weaker. The network is <em>trained</em>, for example by developing stronger connections between nodes with simultaneously high values. This year’s laureates have conducted important work with artificial neural networks from the 1980s onward.</p> <p><strong>John Hopfield</strong> invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The <em>Hopfield network</em> utilises 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.</p> <p><strong>Geoffrey Hinton</strong> used the Hopfield network as the foundation for a new network that uses a different method: the <em>Boltzmann machine</em>. 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 Boltzmann 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.</p> <p>“The laureates’ work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties,” says Ellen Moons, Chair of the Nobel Committee for Physics.</p> <h3 class="wp-block-heading">Illustrations</h3> <p>The illustrations are free to use for non-commercial purposes. Attribute ”©Johan Jarnestad/The Royal Swedish Academy of Sciences”.</p> <p><a href="https://www.nobelprize.org/uploads/2024/10/fig1_fy_24_svartvit.pdf" target="_blank" rel="noreferrer noopener">Illustration: The Nobel Prize in Physics 2024 (pdf)</a><br /><a href="https://www.nobelprize.org/uploads/2024/10/fig2_fy_pop_en_241009_neuronVSnod-2.pdf" target="_blank" rel="noreferrer noopener">Illustration: Natural and artificial neurons (pdf)</a><br /><a href="https://www.nobelprize.org/uploads/2024/10/fig3_fy_en_24.pdf" target="_blank" rel="noreferrer noopener">Illustration: Memories are stored in a landscape (pdf)</a><br /><a href="https://www.nobelprize.org/uploads/2024/10/fig4_fy_en_24.pdf" target="_blank" rel="noreferrer noopener">Illustration: Different types of network (pdf)</a></p> <h3 class="wp-block-heading">Read more about this year’s prize</h3> <p><a href="https://www.nobelprize.org/uploads/2024/11/popular-physicsprize2024-3.pdf" target="_blank" rel="noreferrer noopener">Popular science background: They used physics to find patterns in information (pdf)</a><br /><a href="https://www.nobelprize.org/uploads/2024/11/advanced-physicsprize2024-3.pdf" target="_blank" rel="noreferrer noopener">Scientific background: “For foundational discoveries and inventions that enable machine learning with artificial neural networks” (pdf)</a></p> <hr class="wp-block-separator has-alpha-channel-opacity"/> <p><strong>John J. Hopfield</strong>, born 1933 in Chicago, IL, USA. PhD 1958 from Cornell University, Ithaca, NY, USA. Professor at Princeton University, NJ, USA.</p> <p><strong>Geoffrey Hinton</strong>, born 1947 in London, UK. PhD 1978 from The University of Edinburgh, UK. Professor at University of Toronto, Canada.</p> <hr class="wp-block-separator has-alpha-channel-opacity"/> <p><strong>Prize amount</strong>: 11 million Swedish kronor, to be shared equally between the laureates.<strong><br />Further information</strong>: www.kva.se and www.nobelprize.org<strong><br />Press contact</strong>: Eva Nevelius, Press Secretary, +46 70 878 67 63, <a href="/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="f4918295da9a918291989d8187b49f8295da8791">[email protected]</a><br /><strong>Experts</strong>: Olle Eriksson, +46 18 471 36 25, <a href="/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="690605050c470c1b00021a1a0607291901101a000a1a471c1c471a0c">[email protected]</a> and Anders Irbäck, +46 46 222 34 93, <a href="/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="debfb0babbacadf0b7acbcbfbdb59ebdbbbdf0b2abf0adbb">[email protected]</a>, members of the Nobel Committee for Physics. </p> <hr class="wp-block-separator has-alpha-channel-opacity"/> <p class="has-16-font-size">The Royal Swedish Academy of Sciences, founded in 1739, is an independent organisation whose overall objective is to promote the sciences and strengthen their influence in society. 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