Nobel Prize in Physics Awarded for Pioneering AI Research

Nobel physics prize awarded for pioneering AI research by 2 scientists

By Derrick Bryson Taylor, Cade Metz and Katrina Miller NYT News Service/Syndicate Stories John J. Hopfield and Geoffrey E. Hinton received the Nobel Prize in physics Tuesday for discoveries that helped computers learn more in the way the human brain does, providing the building blocks for developments in artificial intelligence. The award is an acknowledgment of AI's growing significance in the way people live and work. With their ability to make sense of vast amounts of data, artificial neural networks already have a major role in scientific research, the Nobel committee said, including in physics, where it is used to design new materials, crunch large amounts of data from particle accelerators and help survey the universe. The machine learning breakthroughs of Hopfield and Hinton "have showed a completely new way for us to use computers to aid and to guide us to tackle many of the challenges our society face," the Nobel committee said. Neural networks -- systems that learn skills by analyzing data and are named after the web of neurons in the human brain -- are a part of everyday internet services, including search engines like Google, talking digital assistants like Apple's Siri and chatbots like OpenAI's ChatGPT. These services are rooted in mathematics and computer science, not physics. But research by Hopfield and Hinton in the late 1970s and early 1980s helped influence the development of the digital neural networks that have become part of the fabric of the modern internet. "If there was a Nobel Prize for computer science, our work would clearly be more appropriate for that," Hinton, a recipient of the 2018 Turing Award who has been called the "godfather of AI," said in a phone interview with The New York Times. "But there isn't one." Hinton left his job as a researcher at Google last year, in part so that he could freely discuss his concerns that the AI technologies he helped create could end up harming humanity. In a call during the Nobel announcement in Stockholm on Tuesday, Hinton expressed worries over machine learning and said it would have an extraordinary influence on society. "It will be comparable with the Industrial Revolution," he said. "Instead of exceeding people in physical strength, it's going to exceed people in intellectual ability. We have no experience of what it's like to have things smarter than us." While Hinton expressed his concerns, he also said that the advanced technology would bring much better health care. "It'll mean huge improvements in productivity," he said. "But we also have to worry about a number of possible bad consequences, particularly the threat of these things getting out of control." Speaking with the Times, he said that winning the Nobel Prize could bring more attention to his concern about the future of the technology. "Having the Nobel Prize could mean that people will take me more seriously," he said. In a news conference Tuesday, Hopfield compared advances in AI with the splitting of the atom, which led to both deadly bombs and bountiful energy. "One is accustomed to having technologies which are not only good or only bad, but have capabilities in both directions," he said. But he added, "You want to have some idea of how you can control the system, and how you can prevent disasters from occurring." 'Ground zero' for modern AI Since it was first awarded in 1901, the Nobel Prize in physics has honored research from the discovery of subatomic particles to gravitational waves and supermassive black holes. But in some years, the committee has acknowledged the application of physics to other disciplines, like in 2021 for work contributing to understanding of climate change. For this year's award, the committee emphasized the way that Hopfield's and Hinton's work in biology and computer science had roots in the physical sciences. While it may seem an unusual fit under the umbrella of physics, Dmitry Krotov, a physicist with the Massachusetts Institute of Technology and IBM who has published several papers with Hopfield in recent years, said the boundaries between fields were "somewhat artificial," adding that "what is nice about physics is that historically, it is always expanding." Hopfield, a Chicago native, is an emeritus professor at Princeton University known for seminal discoveries in computer science, biology and physics. He is 91, and the third oldest Nobel physics laureate. He began his career at Bell Laboratories in 1958 as a physicist studying the properties of solid matter, but felt limited by the boundaries of his field. He moved to the University of California, Berkeley, as an assistant professor in 1961 and joined the physics faculty at Princeton in 1964. Sixteen years later, he moved to the California Institute of Technology as a professor of chemistry and biology, and in 1997, returned to Princeton, this time in the department of molecular biology. In the 1980s, his work focused on how the processes of the brain can inform how machines save and reproduce patterns. He explained in an interview that his work came from an initial intrigue with the connections between physics and biology. "Biology is just a physical system, but a very complicated one," he said. In 1982, Hopfield developed a model of neural networks, today known as the Hopfield network, to describe how the brain recalls memories when fed partial information, similar to the method your brain uses to remember a word on the tip of your tongue. This ability is called associative memory. In describing the Hopfield network's nodes and their linkages, Hopfield's work showed that their behavior resembled the physics that explains how the spins of nearby atoms affect one another. He did not anticipate that his work on neural networks would ever be useful in machine learning. But there's a "natural handshake" between questions in AI and biology, he said. The years leading up to the Hopfield network were like an "AI winter," Krotov said. But Hopfield's work in 1982 "was the major driving force that ended that period," he said. He continued, "It's the ground zero for the modern era of neural networks." This point was affirmed by neuroscientist and computer scientist Terry Sejnowski, now at the Salk Institute for Biological Sciences, who studied under Hopfield and later became a key collaborator of Hinton's. Work on the Hopfield network, "drew many physicists into the machine learning field," he said. "In many ways, it helped create the field." The road to chatbots Hinton, born just outside London, has lived and worked mostly in the United States and Canada since the late 1970s. He is a professor of computer science at the University of Toronto. Hinton, 76, began researching neural networks as a graduate student at the University of Edinburgh in the early 1970s, a time when few researchers believed in the idea. In 1985, Hinton and his colleagues developed a new neural network they named the Boltzmann machine. As with Hopfield's research, the nodes in Hinton's Boltzmann machine could be described with physics. But instead of spin, they used the Boltzmann equation, named for statistical physics pioneer Ludwig Boltzmann. The equation describes the energy of a system. Yann LeCun, the chief AI scientist at Meta, pointed out that the Hopfield network and Boltzmann machine were not used by modern AI technologies. But he said that modern technologies were very much influenced by these early, physics-related creations from Hopfield and Hinton. Their work, he explained, inspired many scientists to begin exploring neural networks, which most academics had previously dismissed as a scientific dead end. "It made the whole neural net field kosher again," he said. "Before this, it was taboo." Following on the Boltzmann work, Hinton and his collaborators developed a new form of neural network based on a mathematical idea called "backpropagation." He and others, including LeCun, nurtured this idea for the next few decades, largely at universities in Canada and Europe. Ultimately, Hinton and two of his graduate students at the University of Toronto made a breakthrough with the technology in 2012, and he joined Google. More recently, he shared the Turing Award with LeCun as well as Yoshua Bengio, a professor of computer science at the University of Montreal whose research focuses on ensuring that AI is developed safely. Packed inboxes, canceled appointments Neither man was expecting to be named a Nobel physics laureate. Hopfield said he was "astonished," when asked in an interview with the Times about how he felt about winning the Nobel Prize. Currently in England, he was out getting a flu shot and having coffee during the announcement. He came home to an inbox overflowing with congratulatory messages. "I've never had so much email before in my life," Hopfield said, adding that it took some digging for him to discover what, exactly, he was being congratulated for. Hinton said he learned of the prize while staying in a "cheap hotel" in California. "I was going to get an MRI scan today, but I think I'll have to cancel that," he said. Who received the 2023 Nobel Prize in physics? The prize was shared by Pierre Agostini, Ferenc Krausz and Anne L'Huillier for work that let scientists capture the motions of subatomic particles moving at impossible speeds. Who else has received a Nobel Prize in the sciences this year? On Monday, the prize in physiology or medicine went to Victor Ambros and Gary Ruvkun for their discovery of microRNA, which helps determine how cells develop and function. When will the other Nobel Prizes be announced? ▪ The Nobel Prize in chemistry will be awarded Wednesday by the Royal Swedish Academy of Sciences in Stockholm. Last year, the prize went to Moungi G. Bawendi, Louis E. Brus and Alexei I. Ekimov for discovering and developing quantum dots that are expected to lead to advances in electronics, solar cells and encrypted quantum information. ▪ The Nobel Prize in literature will be awarded Thursday by the Swedish Academy in Stockholm. Last year, Jon Fosse of Norway was honored for plays and prose that gave "voice to the unsayable." ▪ The Nobel Peace Prize will be awarded Friday by the Norwegian Nobel Institute in Oslo. Last year, Narges Mohammadi, an activist in Iran, was recognized "for her fight against the oppression of women in Iran and her fight to promote human rights and freedom for all." Mohammadi is serving a 10-year sentence in an Iranian prison, where her attorneys have raised concerns about her well-being. ▪ The Nobel Memorial Prize in Economic Sciences will be awarded Monday by the Royal Swedish Academy of Sciences in Stockholm. Last year, Claudia Goldin was awarded for her research uncovering the reasons for gender gaps in labor force participation and earnings. All of the prize announcements are streamed live by the Nobel Prize organization.

Nobel Prize in physics awarded to two scientists for discoveries that enable machine learning

John Hopfield and Geoffrey Hinton were awarded the Nobel Prize in physics Tuesday for discoveries and inventions that formed the building blocks of machine learning. "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," the Nobel committee said in a press release. Hopfield's research is carried out at Princeton University and Hinton works at the University of Toronto. Breaking news: This article is an initial update and will be expanded with more details shortly. Nobel committee announcement: The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Physics 2024 to "for foundational discoveries and inventions that enable machine learning with artificial neural networks" They trained artificial neural networks using physics 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. 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 trained, 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. John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network 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. 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 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. "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. The Nobel Prize in Physics 2024 This year's laureates used tools from physics to construct methods that helped lay the foundation for today's powerful machine learning. John Hopfield created a structure that can store and reconstruct information. Geoffrey Hinton invented a method that can independently discover properties in data and which has become important for the large artificial neural networks now in use. They used physics to find patterns in information Many people have experienced how computers can translate between languages, interpret images and even conduct reasonable conversations. What is perhaps less well known is that this type of technology has long been important for research, including the sorting and analysis of vast amounts of data. The development of machine learning has exploded over the past fifteen to twenty years and utilises a structure called an artificial neural network. Nowadays, when we talk about artificial intelligence, this is often the type of technology we mean. Although computers cannot think, machines can now mimic functions such as memory and learning. This year's laureates in physics have helped make this possible. Using fundamental concepts and methods from physics, they have developed technologies that use structures in networks to process information. Machine learning differs from traditional software, which works like a type of recipe. The software receives data, which is processed according to a clear description and produces the results, much like when someone collects ingredients and processes them by following a recipe, producing a cake. Instead of this, in machine learning the computer learns by example, enabling it to tackle problems that are too vague and complicated to be managed by step by step instructions. One example is interpreting a picture to identify the objects in it. Mimics the brain An artificial neural network processes information using the entire network structure. The inspiration initially came from the desire to understand how the brain works. In the 1940s, researchers had started to reason around the mathematics that underlies the brain's network of neurons and synapses. Another piece of the puzzle came from psychology, thanks to neuroscientist Donald Hebb's hypothesis about how learning occurs because connections between neurons are reinforced when they work together. Later, these ideas were followed by attempts to recreate how the brain's network functions by building artificial neural networks as computer simulations. In these, the brain's neurons are mimicked by nodes that are given different values, and the synapses are represented by connections between the nodes that can be made stronger or weaker. Donald Hebb's hypothesis is still used as one of the basic rules for updating artificial networks through a process called training. At the end of the 1960s, some discouraging theoretical results caused many researchers to suspect that these neural networks would never be of any real use. However, interest in artificial neural networks was reawakened in the 1980s, when several important ideas made an impact, including work by this year's laureates. Associative memory Imagine that you are trying to remember a fairly unusual word that you rarely use, such as one for that sloping floor often found in cinemas and lecture halls. You search your memory. It's something like ramp... perhaps rad...ial? No, not that. Rake, that's it! This process of searching through similar words to find the right one is reminiscent of the associative memory that the physicist John Hopfield discovered in 1982. The Hopfield network can store patterns and has a method for recreating them. When the network is given an incomplete or slightly distorted pattern, the method can find the stored pattern that is most similar. Hopfield had previously used his background in physics to explore theoretical problems in molecular biology. When he was invited to a meeting about neuroscience he encountered research into the structure of the brain. He was fascinated by what he learned and started to think about the dynamics of simple neural networks. When neurons act together, they can give rise to new and powerful characteristics that are not apparent to someone who only looks at the network's separate components. In 1980, Hopfield left his position at Princeton University, where his research interests had taken him outside the areas in which his colleagues in physics worked, and moved across the continent. He had accepted the offer of a professorship in chemistry and biology at Caltech (California Institute of Technology) in Pasadena, southern California. There, he had access to computer resources that he could use for free experimentation and to develop his ideas about neural networks. However, he did not abandon his foundation in physics, where he found inspiration for his understanding of how systems with many small components that work together can give rise to new and interesting phenomena. He particularly benefitted from having learned about magnetic materials that have special characteristics thanks to their atomic spin - a property that makes each atom a tiny magnet. The spins of neighbouring atoms affect each other; this can allow domains to form with spin in the same direction. He was able to make a model network with nodes and connections by using the physics that describes how materials develop when spins influence each other. The network saves images in a landscape The network that Hopfield built has nodes that are all joined together via connections of different strengths. Each node can store an individual value - in Hopfield's first work this could either be 0 or 1, like the pixels in a black and white picture. Hopfield described the overall state of the network with a property that is equivalent to the energy in the spin system found in physics; the energy is calculated using a formula that uses all the values of the nodes and all the strengths of the connections between them. The Hopfield network is programmed by an image being fed to the nodes, which are given the value of black (0) or white (1). The network's connections are then adjusted using the energy formula, so that the saved image gets low energy. When another pattern is fed into the network, there is a rule for going through the nodes one by one and checking whether the network has lower energy if the value of that node is changed. If it turns out that energy is reduced if a black pixel is white instead, it changes colour. This procedure continues until it is impossible to find any further improvements. When this point is reached, the network has often reproduced the original image on which it was trained. This may not appear so remarkable if you only save one pattern. Perhaps you are wondering why you don't just save the image itself and compare it to another image being tested, but Hopfield's method is special because several pictures can be saved at the same time and the network can usually differentiate between them. Hopfield likened searching the network for a saved state to rolling a ball through a landscape of peaks and valleys, with friction that slows its movement. If the ball is dropped in a particular location, it will roll into the nearest valley and stop there. If the network is given a pattern that is close to one of the saved patterns it will, in the same way, keep moving forward until it ends up at the bottom of a valley in the energy landscape, thus finding the closest pattern in its memory. The Hopfield network can be used to recreate data that contains noise or which has been partially erased. Hopfield and others have continued to develop the details of how the Hopfield network functions, including nodes that can store any value, not just zero or one. If you think about nodes as pixels in a picture, they can have different colours, not just black or white. Improved methods have made it possible to save more pictures and to differentiate between them even when they are quite similar. It is just as possible to identify or reconstruct any information at all, provided it is built from many data points. Classification using nineteenth-century physics Remembering an image is one thing, but interpreting what it depicts requires a little more. Even very young children can point at different animals and confidently say whether it is a dog, a cat, or a squirrel. They might get it wrong occasionally, but fairly soon they are correct almost all the time. A child can learn this even without seeing any diagrams or explanations of concepts such as species or mammal. After encountering a few examples of each type of animal, the different categories fall into place in the child's head. People learn to recognise a cat, or understand a word, or enter a room and notice that something has changed, by experiencing the environment around them. When Hopfield published his article on associative memory, Geoffrey Hinton was working at Carnegie Mellon University in Pittsburgh, USA. He had previously studied experimental psychology and artificial intelligence in England and Scotland and was wondering whether machines could learn to process patterns in a similar way to humans, finding their own categories for sorting and interpreting information. Along with his colleague, Terrence Sejnowski, Hinton started from the Hopfield network and expanded it to build something new, using ideas from statistical physics. Statistical physics describes systems that are composed of many similar elements, such as molecules in a gas. It is difficult, or impossible, to track all the separate molecules in the gas, but it is possible to consider them collectively to determine the gas' overarching properties like pressure or temperature. There are many potential ways for gas molecules to spread through its volume at individual speeds and still result in the same collective properties. The states in which the individual components can jointly exist can be analysed using statistical physics, and the probability of them occurring calculated. Some states are more probable than others; this depends on the amount of available energy, which is described in an equation by the nineteenth-century physicist Ludwig Boltzmann. Hinton's network utilised that equation, and the method was published in 1985 under the striking name of the Boltzmann machine. Recognising new examples of the same type The Boltzmann machine is commonly used with two different types of nodes. Information is fed to one group, which are called visible nodes. The other nodes form a hidden layer. The hidden nodes' values and connections also contribute to the energy of the network as a whole. The machine is run by applying a rule for updating the values of the nodes one at a time. Eventually the machine will enter a state in which the nodes' pattern can change, but the properties of the network as a whole remain the same. Each possible pattern will then have a specific probability that is determined by the network's energy according to Boltzmann's equation. When the machine stops it has created a new pattern, which makes the Boltzmann machine an early example of a generative model. The Boltzmann machine can learn - not from instructions, but from being given examples. It is trained by updating the values in the network's connections so that the example patterns, which were fed to the visible nodes when it was trained, have the highest possible probability of occurring when the machine is run. If the same pattern is repeated several times during this training, the probability for this pattern is even higher. Training also affects the probability of outputting new patterns that resemble the examples on which the machine was trained. A trained Boltzmann machine can recognise familiar traits in information it has not previously seen. Imagine meeting a friend's sibling, and you can immediately see that they must be related. In a similar way, the Boltzmann machine can recognise an entirely new example if it belongs to a category found in the training material, and differentiate it from material that is dissimilar. In its original form, the Boltzmann machine is fairly inefficient and takes a long time to find solutions. Things become more interesting when it is developed in various ways, which Hinton has continued to explore. Later versions have been thinned out, as the connections between some of the units have been removed. It turns out that this may make the machine more efficient. During the 1990s, many researchers lost interest in artificial neural networks, but Hinton was one of those who continued to work in the field. He also helped start the new explosion of exciting results; in 2006 he and his colleagues Simon Osindero, Yee Whye Teh and Ruslan Salakhutdinov developed a method for pretraining a network with a series of Boltzmann machines in layers, one on top of the other. This pretraining gave the connections in the network a better starting point, which optimised its training to recognise elements in pictures. The Boltzmann machine is often used as part of a larger network. For example, it can be used to recommend films or television series based on the viewer's preferences. Machine learning - today and tomorrow Thanks to their work from the 1980s and onward, John Hopfield and Geoffrey Hinton have helped lay the foundation for the machine learning revolution that started around 2010. The development we are now witnessing has been made possible through access to the vast amounts of data that can be used to train networks, and through the enormous increase in computing power. Today's artificial neural networks are often enormous and constructed from many layers. These are called deep neural networks and the way they are trained is called deep learning. A quick glance at Hopfield's article on associative memory, from 1982, provides some perspective on this development. In it, he used a network with 30 nodes. If all the nodes are connected to each other, there are 435 connections. The nodes have their values, the connections have different strengths and, in total, there are fewer than 500 parameters to keep track of. He also tried a network with 100 nodes, but this was too complicated, given the computer he was using at the time. We can compare this to the large language models of today, which are built as networks that can contain more than one trillion parameters (one million millions). Many researchers are now developing machine learning's areas of application. Which will be the most viable remains to be seen, while there is also wide-ranging discussion on the ethical issues that surround the development and use of this technology. Because physics has contributed tools for the development of machine learning, it is interesting to see how physics, as a research field, is also benefitting from artificial neural networks. Machine learning has long been used in areas we may be familiar with from previous Nobel Prizes in Physics. These include the use of machine learning to sift through and process the vast amounts of data necessary to discover the Higgs particle. Other applications include reducing noise in measurements of the gravitational waves from colliding black holes, or the search for exoplanets. In recent years, this technology has also begun to be used when calculating and predicting the properties of molecules and materials - such as calculating protein molecules' structure, which determines their function, or working out which new versions of a material may have the best properties for use in more efficient solar cells. © 2024 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed without permission.

AIP Congratulates 2024 Nobel Prize Winners in Phys | Newswise

WASHINGTON, Oct. 8, 2024 - The 2024 Nobel Prize in physics was awarded to John J. Hopfield and Geoffrey E. Hinton "for foundational discoveries and inventions that enable machine learning with artificial neural networks." "Beyond recognizing the laureates' inspirations from condensed-matter physics and statistical mechanics, the prize celebrates interdisciplinarity," said Michael Moloney, CEO of AIP. "At its core, this prize is about how elements of physics have driven the development of computational algorithms to mimic biological learning, impacting how we make discoveries today across STEM. And it also demonstrates that fundamental shifts in our scientific understanding can sometimes take decades to have wider impact." While computers are powerful, they traditionally struggle with some tasks, such as pattern recognition, that humans and other mammals excel at. Our brains are collections of neurons, linked together into dynamic networks with connections of variable strength, that can rapidly identify patterns in data and learn so that those patterns can be recalled. Hopfield and Hinton created similar networks, termed "artificial neural networks," that led to many of the exciting computational developments of the last few years. Hopfield is the Howard A. Prior Professor of Life Sciences and professor of molecular biology at Princeton University, and in 2006 he served as president of the American Physical Society, an AIP Member Society. In 1982, Hopfield defined one of the first instances of an artificial neural network. Named the Hopfield network, his design leverages techniques from statistical mechanics to create a form of associative memory. When a Hopfield network is exposed to a stimulus, pairs of neurons fire simultaneously, strengthening the connection between them, in a manner analogous to how connections form between biological neurons. Subsequently, this neural network can rely on these strengthened connections to recognize the initial stimulus, even from incomplete or noisy data. It "remembers" the pattern, so to speak. Hinton is a professor of computer science at the University of Toronto. He is a cognitive psychologist and computer scientist, celebrated for his groundbreaking contributions to artificial neural networks. Often referred to as the "Godfather of AI," Hinton explored the application of neural networks in machine learning, memory, perception, and symbol processing. Hinton expanded upon the Hopfield network and the application of physics-based concepts through the development of the Boltzmann Machine, a type of unsupervised generative deep learning network capable of identifying distinctive elements within data. By training it on examples that are likely to arise when the machine is run, the Boltzmann machine can classify images and generate new instances of learned patterns. Hinton's work has been instrumental in sparking the rapid advancements in machine learning we see today, decades after his formative work. "AIP Publishing congratulates John Hopfield and Geoffrey Hinton for the Nobel Prize in physics," said AIP Publishing's Chief Publishing Officer, Penelope Lewis. "Their research in understanding and developing artificial neural networks is a testament to the power of interdisciplinary research, combining fundamental concepts in statistical and quantum physics with neuroscience and psychology. This foundational discovery has led to an explosion of applications in machine learning and artificial intelligence in fields ranging from materials science to medical imaging." Access to Experts for Comment and Interviews Experts from AIP and AIP Publishing are available this morning to comment on the new laureates, their accomplishments, and the importance of the Nobel award to the world of science at large. Interviews and quotes can be obtained by contacting [email protected] after the Nobel presentation. Physics Today, an AIP publication, will be contributing reporting expertise. There will be a morning briefing and an afternoon comprehensive report on the physics prize posted on its site and sent to weekly email newsletter subscribers. Dedicated Resources Collection A collection of resources and relevant information pertaining to this year's winners and their scientific achievements will be curated throughout the day (and beyond) and will be available at https://ww2.aip.org/aip/nobel-physics-resources-2024. The AIP team will update the collection as information, assets, and resources are uncovered concerning the winning science. As a 501(c)(3) non-profit, AIP is a federation that advances the success of our Member Societies and an institute that engages in research and analysis to empower positive change in the physical sciences. The mission of AIP (American Institute of Physics) is to advance, promote, and serve the physical sciences for the benefit of humanity. About AIP Publishing AIP Publishing's mission is to advance, promote, and serve the physical sciences for the benefit of humanity by breaking barriers to open, equitable research communication and empowering researchers to accelerate global progress. AIP Publishing is a wholly owned not-for-profit subsidiary of the American Institute of Physics (AIP) and supports the charitable, scientific, and educational purposes of AIP through scholarly publishing activities on its behalf and on behalf of our publishing partners.