How Physics Meets Machine Learning in AI

Quantum Leap in Machine Learning? ⚛️📈 The Rise of Physics-Aware AI We're witnessing a fascinating convergence in the world of AI: the integration of fundamental physics principles into machine learning models. Forget just throwing data at algorithms; we're now building AI that understands the underlying physical laws governing the systems they're analyzing. This isn't just about better predictions; it's about creating more robust, reliable, and explainable AI. This approach, often called Physics-Aware AI or Physics-Informed Neural Networks (PINNs), is particularly powerful in fields like climate modeling, materials science, and even drug discovery. By embedding physical equations (like fluid dynamics or thermodynamic laws) directly into the model's architecture, we can significantly reduce the amount of training data required and improve generalization to unseen scenarios. Here's what makes this so exciting: 🧪 Improved Accuracy: Integrating physics constraints leads to more realistic and accurate simulations, especially in complex systems. 🧠 Enhanced Explainability: Because the models are grounded in physics, we gain a better understanding of why they're making certain predictions. No more black boxes! 🚀 Data Efficiency: Less reliance on massive datasets makes AI accessible to domains where data is scarce or expensive to acquire. 🛡️ Robustness and Generalization: Physics-aware models are less prone to overfitting and can perform better on out-of-distribution data. 🌍 Real-World Impact: Huge potential in tackling global challenges like climate change, developing new materials, and accelerating scientific discovery. What potential applications of Physics-Aware AI are you most excited about? Let's discuss in the comments! #Technology #Innovation #AI #MachineLearning #PhysicsInformedAI #PINNs #ScientificComputing #FutureofAI

The secret's out: Artificial Intelligence and Machine Learning are not just about algorithms and data - they're deeply rooted in mathematical innovations, particularly in the fascinating realm of Differential Geometry. Have you ever wondered how AI-driven mathematical breakthroughs can unlock the mysteries of curvature, manifolds, and higher-dimensional spaces? As we explore this uncharted territory, we begin to unravel the immense potential of AI/ML in tackling complex problems. Differential Geometry, with its concepts of manifolds and curvature, plays a vital role in understanding the intricacies of high-dimensional data. For instance, techniques like Geometric Deep Learning are being used to analyze and process data on curved spaces, leading to breakthroughs in image and signal processing. Some key areas where Differential Geometry is making an impact include: * Geometric Deep Learning for computer vision and image processing * Riemannian Geometry for optimization and machine learning * Topology for understanding the shape and structure of data But here's the million-dollar question: can AI-driven mathematical innovations unlock the secrets of the universe, from the intricacies of spacetime to the mysteries of quantum mechanics? I'd love to hear your thoughts on this! Have you explored the intersection of Differential Geometry and AI/ML? Share your experiences and let's spark a conversation! #AI #ML #DifferentialGeometry #Mathematics #Innovation

I am excited to share our new preprint, "Robust and Compact Neural Computation via Hyperbolic Geometry." This work addresses two critical challenges in modern deep learning: model brittleness and excessive parameterization. We introduce the Hyperbolic Network (HyperNet), an architecture that embeds data into a Poincaré Ball manifold and performs classification using the native Poincaré distance metric. Our central hypothesis is that the inductive bias of hyperbolic space can lead to more robust and efficient representations compared to standard Euclidean models. Our experiments on MNIST provide strong evidence for this hypothesis. We compared our HyperNet to a standard CNN baseline and found: Accuracy: The CNN performed better on clean data (98.73% vs. 94.79%). Size: Our HyperNet was 2x smaller (403 KB vs. 811 KB). Robustness: Under extreme Gaussian noise (σ=0.6), our HyperNet's accuracy was 82.70%, while the baseline CNN's dropped to 40.81%. A 2x improvement in robustness from a model half the size presents a compelling case for exploring non-Euclidean geometries as a foundational principle for building next-generation AI systems. The full paper is available for review. I welcome any discussion, feedback, or collaboration. Link: https://lnkd.in/eZmwQZdT #AIResearch #MachineLearning #GeometricDeepLearning #RobustAI #ComputerScience #Paper

Day 7: AI Winter and Renaissance - The Rollercoaster Decades Reflecting on the tumultuous journey of AI through the decades, it's evident that progress in Artificial Intelligence has not followed a straight path. From the pioneering days at the Dartmouth Conference in the late 1950s to the current Deep Learning Revolution, the field has weathered numerous "winters" and "springs." In the Golden Years of AI, spanning from 1956 to 1973, the term was first coined, and early successes in logic theorems and game playing fueled optimism with massive government funding. However, the First AI Winter from 1974 to 1980 brought challenges as computers struggled with speed and memory limitations, leading to a funding drought and researcher exodus. The 1980s saw the rise of Expert Systems with significant investments before another crash, followed by the Second AI Winter from 1987 to 1993 due to the inability of these systems to handle real-world complexity. Gradual Recovery from 1993 to 2010 brought better algorithms and the promise of machine learning, supported by the internet's vast datasets. The current era, spanning from 2010 to the present, marks the Deep Learning Revolution, propelled by GPU computing and the synergy of big data and computing power, which has ushered in breakthrough results and the resurgence of AI. The key lesson learned Is That Progress in AI is nonlinear, with persistence through challenges leading to innovation. Looking ahead, what contemporary challenges reminiscent of "AI winters" do you observe in today's AI landscape? #AIHistory #AIWinter #Innovation #MachineLearning #100DaysOfAI #TechEvolution

Selective State Space Models (SSMs): a real alternative to Transformers? Transformers dominate deep learning, but recent research points to another path: selective state space architectures, such as Mamba. The idea is to replace quadratic attention with linear recurrent mechanisms, without losing expressive power. 🔹 How it works • Mamba is based on SSMs like S4, but with adaptive selectivity. • Parameters change according to the input, allowing the network to decide when to propagate or forget information. This ensures linear complexity for long sequences, up to 5× higher throughput than equivalent Transformers, and comparable performance in language benchmarks. 🔹 Recent developments • MambaLRP (2024): applies explainability techniques (layer-wise relevance propagation), bringing transparency to state space models. • MambaMixer (2024): extends selectivity to tokens and channels, with results in computer vision and time series. • SeRpEnt (2025): proposes selective resampling, compressing sequences without significant quality loss. 🔹 Strengths • Linear scalability → context of millions of tokens. • Cheaper inference on limited hardware. • Variants already explored in vision, time, and multimodality. 🔹 Challenges • Less mature tools compared to the Transformer ecosystem. • Risk of incorrect “forgetting” if input patterns diverge. • Interpretability still in its early stages, despite advances like MambaLRP. 🔹 Expected impact • Selective SSMs can enable faster and more accessible models for text, genomics, biomedical signals, and multimodality. If proven robust at scale, they may become the next AI backbone — reducing dependence on attention. #DeepLearning #AI #StateSpaceModels #Mamba #ModelArchitectures #Innovation

🚀 Thrilled to share my latest project – Real-Time Object Tracking with CNNs! 🎯 In this project, I developed a system that not only detects but also tracks objects across video frames in real time. Explore the app 👇 https://lnkd.in/gA3uQqiq 🔍 Why CNN? At the core of object detection and tracking lies the Convolutional Neural Network (CNN). CNNs are designed to process images efficiently by: 📌 Convolution Layers: Extracting spatial features like edges, textures, and patterns. 📌 Pooling Layers: Reducing dimensions while retaining critical information. 📌 Fully Connected Layers: Performing classification to recognize objects. These hierarchical layers enable CNNs to understand images at multiple levels, making them ideal for object detection, recognition, and tracking. ✨ Project Highlights: 🔹 Used CNN-powered models (YOLO) for accurate detection. 🔹 Extended detection into tracking by assigning unique IDs to objects and following their trajectories across frames. 🔹 Built an interactive Streamlit interface for easy visualization and testing. 🔹 Gained deeper understanding of how CNN features evolve from low-level edges to high-level object semantics. This project not only strengthened my knowledge in Deep Learning & Computer Vision but also showcased how CNNs serve as the backbone for real-world AI applications like surveillance, traffic monitoring, and autonomous systems. Upender Muthyala Raghu Ram Aduri Innomatics Research Labs #ObjectTracking #ComputerVision #DeepLearning #ArtificialIntelligence #CNN #YOLO #MachineLearning #RealTimeAI #AIProjects #OpenCV #Streamlit #DataScience #NeuralNetworks #DeepLearningProjects

I agree with the claim that text alone is not enough for AI to understand the world. But vision alone is not enough either! Humans perceive reality through multiple senses: sight, sound, smell, taste, touch, as well as vestibular and proprioceptive inputs. Video by itself cannot fully capture or convey many physical phenomena. That is why engineering more complex systems founded on continuous multimodal learning is crucial. https://lnkd.in/eg7Y_ykE

🧠 **Are Machines Our New Mathematical Visionaries?** We've long relied on our intellect to crack complex equations, but AI is proving to be the ace up our sleeves. Here are the essential insights you need to know: 1. 🌍 **Revolutionizing Understanding**: AI has pushed the boundaries by solving the Navier Stokes equations—critical in fields like meteorology and aerospace—which have challenged mathematicians for over a century. 2. 🔍 **New Frontiers in Insight**: Using advanced neural networks, AI revealed unprecedented mathematical solutions, discovering unstable singularities that could redefine fluid dynamics understanding. 3. 🤝 **Human + AI Synergy**: This breakthrough exemplifies the unparalleled results when human expertise meets machine intelligence—AI doesn't just compute; it discovers and transforms. 💬 **Which insight intrigues you the most? Share your thoughts below!** 👇 #AIRevolution #Mathematics #Innovation #TechAndScience #FutureOfResearch

💡 AI Meets Fluid Dynamics: A New Chapter Unfolds This week, DeepMind (in collaboration with top universities) revealed a breakthrough that could reshape how we understand the world’s most unruly systems: fluid flow. At its heart: the Navier–Stokes equations — the mathematical laws that govern everything from swirling smoke and ocean currents to airflow over wings and blood flow in our bodies. For over a century, these equations have challenged mathematicians and physicists alike. One of the biggest puzzles? Under extreme conditions, could they “blow up” — i.e. lead to infinite velocities or pressures (singularities)? That’s one of the Clay Institute’s Millennium Prize Problems. 🔍 What’s new DeepMind used Physics-Informed Neural Networks (PINNs) enhanced with mathematical insights to search for and uncover new families of unstable singularities in fluid equations. Remarkably, their results have been validated and shown to follow consistent patterns in key parameters (like the “blow-up” speed λ) as they explore higher orders of instability. Their approach blends deep math and AI, pushing toward computer-assisted proofs — where AI doesn’t just calculate but helps discover solutions that humans then verify. 🔄 Why it matters (beyond the math) Improved modeling of turbulence and fluid behavior = better weather forecasting, climate models, and disaster prediction. Engineering gains: aircraft, ships, wind turbines, energy systems — all depend on mastering fluid flows. In the grand scope: this is a proof point that AI can partner in the frontier of fundamental science, not just applied tasks. It accelerates the path toward the holy grail: fully resolving whether the Navier–Stokes equations always behave “nicely” or sometimes break. 🚀 A Glimpse into the Future — “Fluid AI”? Imagine if, one day, we could tame turbulence so precisely that fluid flows themselves become information media. In that world: Water, air, or other fluids act like “liquid circuits,” routing signals via controlled vortices or waves. Turbulence shifts from chaotic to a programmable substrate for computing. The difference between AI on silicon and AI in fluids might blur. While that’s still speculative, the DeepMind result is a stepping stone. We’re seeing the early promise of using AI to unlock deep laws—and eventually, to harness those laws in new forms of computing.

🧐 Interested in the latest advances for long-sequence processing? Our survey "State-space Modeling in Long Sequence Processing: A Survey on Recurrence in the Transformer Era" has now been published in Elsevier Neural Networks ✅ 📌 Goal: help researchers and practitioners navigate the rapidly evolving landscape of long-sequence architectures. 🔗 Open-Access here: https://lnkd.in/eerUybgB This updated version includes: ✨ The latest architectures (#Mamba2, #Griffin, #RWKV5/6/7, #GLA , Gated DeltaNet by Songlin Yang et al., …) ✨ Solutions to improve expressivity in Deep State-Space Models, such as #DeltaProduct by Riccardo G., Julien Siems et al. ✨ New benchmark results and performance comparisons ✨ Refined insights on current limitations and emerging directions, such as #onlinelearning and bio-inspired recurrence We review the main ingredients behind today’s models: ▶ Linear & element-wise recurrence ▶ Gating mechanisms ▶ Hybrid #Transformers– #RNNs ▶ Deep #StateSpaceModels A work by Matteo Tiezzi, Michele Casoni, Alessandro Betti, Marco Gori, and Stefano Melacci. Istituto Italiano di Tecnologia Università di Siena #NeuralNetworksJournal #recurrence #statespacemodels #rnn #transformers #longsequences