Parikshit Jain’s Post

This year’s #Nobel Prizes in #Physics and #Chemistry are sending a strong signal: Artificial Intelligence (AI) has moved from being an emerging tool to a driving force behind groundbreaking scientific inventions. The award-winning work by John Hopfield, Geoffrey Hinton, Demis Hassabis, David Baker and John Jumper highlights how AI is transforming diverse fields such as physics, biology, and chemistry. **The 2024 Physics 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 recreate information. Geoffrey Hinton invented a method that can autonomously find properties in data. **This year’s Chemistry prize acknowledges how AI solved one of biology’s toughest mysteries: figuring out the shapes of proteins. Winners in this area are Demis Hassabis and John M. Jumper for protein structure prediction and David Baker for computational protein design.

Yesterday, the Nobel Prize for Physics 2024 was announced, and as expected, it sparked a lot of discussion—some support it, others question whether it belongs in the realm of "hardcore" physics. This reminds me of a quote from Richard Feynman: "𝙄 𝙙𝙤𝙣’𝙩 𝙠𝙣𝙤𝙬 𝙖𝙣𝙮𝙩𝙝𝙞𝙣𝙜 𝙖𝙗𝙤𝙪𝙩 𝙩𝙝𝙚 𝙉𝙤𝙗𝙚𝙡 𝙋𝙧𝙞𝙯𝙚. 𝙄 𝙙𝙤𝙣’𝙩 𝙪𝙣𝙙𝙚𝙧𝙨𝙩𝙖𝙣𝙙 𝙬𝙝𝙖𝙩 𝙞𝙩’𝙨 𝙖𝙡𝙡 𝙖𝙗𝙤𝙪𝙩 𝙤𝙧 𝙬𝙝𝙖𝙩’𝙨 𝙬𝙤𝙧𝙩𝙝 𝙬𝙝𝙖𝙩... 𝙄 𝙖𝙥𝙥𝙧𝙚𝙘𝙞𝙖𝙩𝙚 𝙞𝙩 𝙛𝙤𝙧 𝙩𝙝𝙚 𝙬𝙤𝙧𝙠 𝙩𝙝𝙖𝙩 𝙄 𝙙𝙞𝙙 𝙖𝙣𝙙 𝙛𝙤𝙧 𝙥𝙚𝙤𝙥𝙡𝙚 𝙬𝙝𝙤 𝙖𝙥𝙥𝙧𝙚𝙘𝙞𝙖𝙩𝙚 𝙞𝙩... 𝙄 𝙙𝙤𝙣’𝙩 𝙩𝙝𝙞𝙣𝙠 𝙩𝙝𝙚𝙧𝙚’𝙨 𝙖𝙣𝙮 𝙨𝙚𝙣𝙨𝙚 𝙩𝙤 𝙖𝙣𝙮𝙩𝙝𝙞𝙣𝙜 𝙚𝙡𝙨𝙚." The point here is simple: the value of the work itself should be what matters most. Awards and recognition are part of modern life, yes, but the contribution to scientific progress is what truly counts. Whether it’s theoretical physics, machine learning, or something in between, 𝗶𝗳 𝗶𝘁 𝗽𝘂𝘀𝗵𝗲𝘀 𝘁𝗵𝗲 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀 𝗮𝗻𝗱 𝗵𝗲𝗹𝗽𝘀 𝘂𝘀 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗲 𝗯𝗲𝘁𝘁𝗲𝗿, 𝗶𝘁 𝗱𝗲𝘀𝗲𝗿𝘃𝗲𝘀 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻. This year’s award went to contributions in machine learning and neural networks—fields not traditionally seen as "physics," but look at their impact on how we model and understand complex systems. Rather than dividing ourselves into camps, we should embrace the broader implications for all fields, including physics. At a time of chaos and division in the world, we as the scientific community need to set an example of unity. Our collective goal is to make the world a better place, and that requires respect and openness toward each other's work. Let’s celebrate the breakthroughs—𝙣𝙤 𝙢𝙖𝙩𝙩𝙚𝙧 𝙬𝙝𝙚𝙧𝙚 𝙩𝙝𝙚𝙮 𝙘𝙤𝙢𝙚 𝙛𝙧𝙤𝙢—and continue pushing the frontiers of knowledge together. #NobelPrize2024 #Physics #MachineLearning #AI #NeuralNetworks #ScientificCommunity #UnityInScience

As significant as the contribution of Restricted Boltzmann Machines to the advancement of AI has been—recognized by the 2024 Nobel Prize in Physics—and as much as this validates my past professional and academic choices, I would like to emphasize the 2024 Nobel Prize in Chemistry awarded for Google DeepMind's research. I recall that DeepMind was founded in 2010 to study how a machine could be taught to play AlphaGo—a goal that seemed almost absurd at the time, both economically and practically, yet has now been recognized with the highest honor one can receive. While I am pleased with the Nobel Committee's choices in the field of Physics, my deepest congratulations go to Demis Hassabis, not only for his intellectual contributions but also for his entrepreneurial courage. #Nobel #AI #Physics #Chemistry #MachineLearning

The NYT [accurately] Calls The Nobel Prize Of Physics–"Computer Science" 10/13/24 This is a well written piece in the NYT on the ethos of AI and very accurately defining the award. Once again, Statistical Mechanics is not a domain of physics–it is a tool to help measure and describe the phenomenon of nature. Link of article in comments. #physics #nobelprize #science #mathematics #ai

John Hopfield and Geoffrey Hinton Win the Nobel Prize in Physics! Both of these scientists are pioneers in AI. However, awarding them the Nobel Prize in Physics is somewhat surprising. They received the prize for machine learning models based on physics: the Hopfield Network and the Boltzmann Machine. While these models do rely on physical principles, their work is more about advancing artificial intelligence rather than contributing directly to the field of physics. This decision may reflect a broader reality: artificial intelligence is now influencing almost every scientific domain, including physics. It raises an important question—has traditional science reached a saturation point in terms of groundbreaking discoveries, or is it that computational technologies are now steering the scientific landscape and major awards like the Nobel Prize? This topic can be explored from a few angles: 1. The Historical Dimension: The Nobel Prize has always been awarded to those who fundamentally change how we understand the world. In this case, the AI models developed by Hopfield and Hinton have transformed our understanding of how machines can "think" and learn, a significant leap in both AI and scientific inquiry. 2. The Future Dimension: Models like the Hopfield Network and the Boltzmann Machine might now be considered classics in the AI field, but with the rapid evolution of AI, it’s possible we will see a resurgence or further developments built on these foundational ideas in the future, as we gain deeper insights into machine learning principles. 3. The Growing Dominance of AI: The fact that AI is extending its influence across all scientific domains raises the question—will we see more Nobel Prizes awarded to AI-related breakthroughs, perhaps even in fields like chemistry or medicine, in the future? Ultimately, the debate surrounding this Nobel Prize isn’t just about the award itself, but about what it signifies for the direction of modern science and the merging of physics with computational intelligence. What do you think? Should AI breakthroughs based on physics receive recognition in the Nobel Prize for Physics, or is it time to create a new Nobel category specifically for Computer Science? #NobelPrize #Physics #ArtificialIntelligence #MachineLearning #NeuralNetworks #GeoffreyHinton #JohnHopfield #Innovation #AIRevolution #PhysicsMeetsAI

Just released: a new Nobel Prize lesson to share with your students. It's a deep dive into the Nobel Prize in Physics 2024, whose laureates have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. Use the #NobelPrize lesson in the classroom and find out more: https://bit.ly/4dyyLpd

Congratulations to CogniGron's Serte Donderwinkel, who will soon be a funded research member at MSRI / Simons Laufer Mathematical Sciences Institute (SLMath) in Berkeley! She has been invited to join their programme 'Probability and Statistics of Discrete Structures' ➡ https://lnkd.in/eJqJs5Ee During her four months in the United States, she will exchange ideas and set up new collaborations on the topic of random networks. Discussions will include challenges and opportunities in the field of neuromorphic computing. 🧠 💻 We are already excited for all the new scientific insights! #mathematics #statistics #computing #physics #ai #neuromorphiccomputing #energyefficiency #CogniGron #FutureProofComputing

Many believe AI is just a passing trend and that it won’t create opportunities or solve problems in tech and non-tech fields. However, recent developments have proven otherwise. Two Nobel Prizes were awarded for groundbreaking AI applications in chemistry and physics. David Baker, Demis Hassabis, and John Jumper received the Nobel Prize in Chemistry for their innovative work on protein design and structure prediction using AI. Similarly, John Hopfield and Geoffrey Hinton, often hailed as pioneers in AI, were honored with the Nobel Prize in Physics for their contributions to machine learning. It’s exciting to see computer science finally being recognized at the Nobel level, even though there’s no specific Nobel Prize for our field! 😄 Read more about their achievements here: Nobel Prize in Chemistry 2024 : Press release: The Nobel Prize in Chemistry 2024 - NobelPrize.org Nobel Prize in Physics 2024: https://lnkd.in/gC76P4MB #AI #ArtificialIntelligence #NobelPrize #Innovation #TechNews #MachineLearning #Chemistry #Physics #TechInnovation #FutureOfAI #ScientificBreakthrough #ComputerScience

From X-rays to AI: Physics is constantly pushing the boundaries of discovery. 🧑🔬🧑💻✨ This year’s Nobel Prize in Physics continues a legacy that began with Wilhelm Röntgen in 1901, whose discovery of X-rays opened up an entirely new way to see the unseen, transforming everything from medical diagnostics to astronomy. Earlier this week, it was announced that John Hopfield and Geoffrey Hinton (known as the “Godfather of AI”) are the 2024 Nobel Prize in Physics laureates. They are making breakthroughs in artificial intelligence and machine learning, using algorithms to sift through immense amounts of data, revealing patterns and insights that were once invisible to the human eye. Their work not only enhances our ability to analyze medical images and scientific data, but also opens up new frontiers in understanding the complexities of quantum mechanics and beyond. The intersection of physics and AI is allowing us to explore the universe in ways that were previously unimaginable. Explore the full list of Nobel laureates in Physics ➡️ https://w.wiki/4Afh

A publication by Professor Marcus Stoffel (left) and Dr. Saurabh Tandale from RWTH’s Chair and Institute of General Mechanics Institut für Allgemeine Mechanik (IAM) - RWTH Aachen University has been selected for Nature Portfolio’s distinguished “Nobel Prize in Physics 2024” collection. This annual compilation showcases outstanding research connected to the Nobel Prize in Physics. John J. Hopfield and Geoffrey E. Hinton are set to receive the 2024 Nobel Prize for physics on December 10 in Stockholm. 👍 The RWTH researchers’ publication, featured in the journal npj Unconventional Computing, is titled “Spiking Neural Networks for Nonlinear Regression of Complex Transient Signals on Sustainable Neuromorphic Processors”. It explores the use of neuromorphic artificial intelligence for sustainable computing processes. Given the high energy demands of artificial intelligence (AI), Professor Marcus Stoffel and Dr. Saurabh Tandale have developed a method for conducting energy-efficient computer simulations using neuromorphic chips and artificial neural networks. This innovative approach enables more sustainable AI-based simulations of engineering structures, directly linking to the groundbreaking discoveries in artificial neural networks recognized by this year’s Nobel Prize in Physics. The publication’s inclusion in this prestigious collection underscores the significance of RWTH’s research and its potential to shape future technologies. See also: Nature portfolio: Nobel Prize in Physics 2024: ➡️ https://lnkd.in/e8tYzS9V Research article in npj Unconventional Computing: Spiking Neural Networks for Nonlinear Regression of Complex Transient Signals on Sustainable Neuromorphic Processors: ➡️ https://lnkd.in/dpX5TjyP 📸: Martin Grüning #nobelprize #Mechanics #artificialintelligence #AI #computersimulations #energy #technologies #engineering #research #science