Science Philanthropy Alliance’s Post

Interesting AI and particle physics breakthrough. AI Breakthrough in Detecting New Particles at the Large Hadron Collider Summary: Artificial intelligence is revolutionizing particle detection in LHC experiments by identifying new physics in particle collisions. At the Rencontres de Moriond conference, CMS physicists showcased AI's ability to differentiate between typical and atypical jets. AI detects potential new particles by analyzing collision data, enhancing sensitivity and accuracy beyond traditional methods. These advancements highlight AI's transformative impact on particle physics research. What does this mean? This breakthrough in particle detection is a game-changer for physics research. Using artificial intelligence, scientists can now sift through massive amounts of data from the Large Hadron Collider much more efficiently. Now, scientists can discover new particles and previously hidden phenomena, opening up exciting possibilities for understanding the universe's mysteries. https://lnkd.in/gXX7Rki6 #AI #ParticlePhysics #LHC #MachineLearning #NewPhysics United States Artificial Intelligence Institute

Physics Gets a Generative AI Boost: Accelerating Discovery A new technique using generative AI could revolutionize the way physicists tackle complex problems! (Source: MIT News) Here's the exciting development: * Automated Phase Classification: This AI can automatically classify phases of physical systems, a traditionally time-consuming process. * Faster Exploration: By automating repetitive tasks, researchers can explore a wider range of possibilities and accelerate scientific discovery. * Improved Material Design: This new approach could lead to the development of novel materials with precisely tailored properties. This MIT research demonstrates the potential of generative AI to propel scientific progress by automating tasks and facilitating exploration in complex fields like physics. #GenerativeAI #Physics #AIforScience #MIT #ScientificDiscovery Read more: https://lnkd.in/dnwJcwKD

CDS PhD student Lily Zhang, with CDS Faculty Fellow Aahlad Manas Puli and CDS Associate Professor Rajesh Ranganath, have introduced a new method to enhance AI's ability to detect rare subatomic particles. Their approach, detailed in Machine Learning: Science and Technology, utilizes a "multi-background" strategy, allowing AI to learn from multiple types of particle interactions. This innovation improves anomaly detection in particle physics, reducing false positives while increasing the likelihood of identifying new physics at the Large Hadron Collider. Zhang emphasizes the importance of a balance between data-driven methods and known physical principles for better detection outcomes. #particlephysics #AI https://lnkd.in/eCRcSea7

Researchers from Massachusetts Institute of Technology and University of Basel developed a clever technique that uses generative AI to automatically detect phase changes in novel materials or complex physical systems. Instead of manually analyzing theoretical models or training on huge datasets, their approach models the underlying statistics from physics simulations to create an AI classifier that can determine what phase a system is in based on parameters like temperature. This physics-informed generative AI method could allow scientists to discover new phases of matter autonomously and more efficiently investigate unique properties of cutting-edge materials. It demonstrates an innovative way to bake known scientific principles into machine learning for enhanced performance on complex analysis tasks. #physics #ai #simulations #machinelearning

🌟 AI + Physics: Ushering in a New Era of Product Innovation 🌟 Recent research, from the paper below, highlights the profound impact AI-driven tools are having on materials science and product development. By enhancing researchers’ ability to explore novel material structures, AI-assisted scientists achieved a remarkable 44% increase in new discoveries, leading to a 39% boost in patent filings and a 17% rise in new product prototypes. 🚀 What makes this especially significant is AI’s capacity to go beyond incremental improvements, enabling breakthroughs that transform entire product lines. Notably, the greatest benefits were seen when top-performing scientists combined AI-suggested materials with their domain expertise, underscoring the power of human-AI collaboration to amplify innovation. This study is a compelling reminder of how strategic integration of AI with human expertise can drive transformative advancements in R&D and product innovation – a promising vision for the future. #AI #Innovation #MaterialsScience #ProductDevelopment #FutureOfWork

We are excited to announce the list of projects you can apply for to join our 2nd PhD cohort in September 2025/26. Applications are open until January 31, 2025. Details are on our website: https://lnkd.in/e468gXm9 - [Sys2RL] Thinking, Fast and Slow: Leveraging Reinforcement Learning to Improve Decision-Making in Generative Models - Multimodal Credal Learning Theory - Human-AI Collaborative Representation Learning for Scientific Data Visualisation - Dynamic ML-Driven Design for Next-Gen Material Design - Employing Spiking Neural Networks to Unravel Spatio-temporal Dynamics of Gene Regulatory Networks using Spatial Transcriptomics Data - AI/ML-Driven Discovery of Hybrid Crystal Organic Semiconductors for Next-Generation Electronic Devices - Designing optimal strategies for controlling complex quantum systems - Optimising Data-Driven Aerosol Emulation: A Decision-Making Framework for Reducing Uncertainty in Climate Simulations - Real-time decision making at 40 MHz at the Large Hadron Collider - Image segmentation in radioastronomy with physical models on graphs - Unpaired Multimodal Representation Learning - Learning theory and methods for novel types of distributional shifts. - Generative sequential experimental design - Causal abstraction of dynamical systems - Goal Driven Learning of Quantum Chemical Energy Surfaces using Multi Fidelity Bayesian Optimization - Spatial-temporal markers of embryo viability for decision making in IVF - Graph-neural networks for computational fluid dynamics - Al-Driven Materials Design for Next-Generation Capacitors - Astrophysical Light Curve Analysis in the Era of Big Data - The Consistent Reasoning Paradox of intelligence in automated decision making: Computing the "I don’t know" function   - Nose-to-tail emulation with nested sampling for online decision making   - A foundation model for C elegans brain dynamics  - Interpretable Self-Sustaining Systems - Privacy and robustness in modern machine learning - Uncertainty-Aware AI Weather Forecasting  - Deriving causal factors from a large-scale decision corpus - Fast Identification of Deformation Mechanisms from HRDIC data using Machine Learning - Multi-objective Bayesian optimisation for networks of vector-valued functions - Uncertainty quantification for multi-physics simulations - Compressed descriptors of damaged microstructures in fusion materials Richard Allmendinger Carl Henrik Ek Maria Karfaki Samuel Kaski Julia Handl Mingfei Sun Wei Pan Anna Scaife Mateja Jamnik Magnus Rattray Neil Lawrence Anirbit Mukherjee Mauricio A. Álvarez

AI Breakthrough in Detecting New Particles at the Large Hadron Collider Artificial intelligence (AI) is transforming particle detection in Large Hadron Collider (LHC) experiments by enabling the identification of potential new physics within particle collisions. AI algorithms are trained to distinguish between typical and atypical jets, helping researchers detect new particles that may explain unresolved physics mysteries. At the Rencontres de Moriond conference, CMS collaboration physicists showcased advancements in using AI to analyze jets, identifying anomalous signatures that could signal new interactions. Different AI training strategies demonstrated varied sensitivities to new particle types, significantly enhancing detection capabilities compared to traditional methods. This progress highlights AI's revolutionary impact on particle physics research. https://lnkd.in/eeggdx-u

Scientists use generative AI to answer complex questions in physics 👩🔬 👩💻 The article from MIT News presents a fascinating development where generative AI is being used to solve complex problems in physics. This research not only demonstrates the capabilities of AI in scientific discovery but also points to a future where AI could become an integral part of the scientific process, leading to faster and more profound advancements in our understanding of the physical world. Dive into the amazing article below to understand that the future is closer than we think! 🚀 Source: https://lnkd.in/eCVjnxaA