Rüya Demirtaş’s Post

🌟 Exciting News from the World of Physics! 🌟 This year’s Nobel Prize in Physics has been awarded to John Hopfield and Geoffrey Hinton for their groundbreaking contributions that laid the foundation for modern machine learning. - John Hopfield developed the Hopfield network, a model that enables the storage and reconstruction of patterns, akin to human associative memory. This network can recognize incomplete or noisy data, making it a powerful tool for data analysis. - Geoffrey Hinton introduced the Boltzmann machine, which learns from examples rather than explicit instructions. By leveraging principles from statistical physics, this model identifies patterns and probabilities within datasets. 🧠 Machine Learning vs. Traditional Software: While traditional software processes data through predetermined steps, machine learning allows computers to learn from examples, tackling complex problems like image recognition and language translation. 🔬 Impact on Physics and Beyond: The methods pioneered by Hopfield and Hinton are revolutionizing fields such as physics, aiding in the analysis of vast datasets for discoveries like the Higgs particle and gravitational waves. Their work is also paving the way for advancements in molecular predictions and material efficiency. 🌐 Looking Ahead: As we continue to harness the power of machine learning, ethical considerations will be paramount. Responsible use of these technologies is crucial for their sustainable development. Congratulations to this year’s laureates for their monumental contributions! 🎉 #NobelPrize #Physics #MachineLearning #ArtificialIntelligence #Innovation #DataScience

Congratulations to Professor John J. Hopfield and Geoffrey Hinton on receiving the Nobel Prize in Physics for their groundbreaking work modeling AI through principles like energy minimization and associative memory. During my research at UCSD on integrating physics with AI, I realized that the fundamental concepts of AI became much clearer solving problems in physics than in traditional courses focused solely on datasets and images. Also it might explain why many Kaggle Grandmasters come from physics backgrounds. As we look ahead, the next wave of AI is poised to integrate physics even more deeply, enhancing our ability to model complex, real-world systems Glad to know both nobel laureates did part of their research at my alma maters, UC San Diego and University of California, Berkeley. #AI #Physics #MachineLearning #DataScience #Kaggle #Research #Innovation #NobelPrize #Technology

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

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

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

Science’s ‘Dance Your Ph.D.’ contest is open again! | 2024 November 27, 2024 Do you have what it takes to win Science’s “Dance Your Ph.D.” competition? As the kangaroo-friendly researcher who was last year’s winner showed, where there’s a will (and a thesis) there’s a dance. As always, we’re challenging scientists to explain their research obsession with fancy footwork, but no PowerPoint slides or jargon. It doesn’t matter whether you’re just starting your Ph.D. or you completed it decades ago; you just need imagination and the ability to keep a beat. The annual contest, in its 17th year and now sponsored by the artificial intelligence and quantum technology company SandboxAQ, features four traditional categories (physics, biology, chemistry, and social science) plus a special category on AI research and quantum science. Winners in each category take home $750. The overall winner gets an extra $2000. In the special category, the dance doesn’t have to be on a personal Ph.D. thesis, but it should still convey a paper, a talk, or research project on AI research and quantum science. More Info: https://lnkd.in/g6dQneF2

Decoding informatics with the power of Physics mindset.

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

The paper "A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery" provides a comprehensive survey of over 250 scientific LLMs, covering various fields such as general science, mathematics, physics, chemistry, materials science, biology, medicine, and geoscience. This broad scope offers a holistic view of how these models are being used across different scientific domains. The paper emphasizes the cross-field and cross-modal connections between scientific LLMs, showing how techniques used in one area can benefit others. The paper highlights how LLMs are deployed to benefit scientific discovery processes, such as hypothesis generation, theorem proving, experiment design, drug discovery, and weather forecasting. #llm #science #AI

Ever wondered where to find young, talented scientists in neuromorphic computing? 👩🏼🎓 🧠 Look no further. The CogniGron Talent Network is where early career researchers come together to face the computing challenges of tomorrow! Meet board member Otavio Citton, whose research interests lie in the realm of complex systems such as computer algorithms, the brain, biological systems and even societies. 💡 He believes that physics can be a powerful tool to better understand these problems, especially statistical mechanics and dynamical systems. Find out more about this network of young scientists ➡ https://lnkd.in/eS4NsZyM #young #scientists #researchers #talent #computing #neuromorphic #braininspired #physics #ai #data #CogniGron #FutureProofComputing