Luminary Cloud raises $72M for Physics AI Model Factory

Sora's Physics Leap: Why Consistency & Real-World Dynamics Are Redefining Generative AI The quest for AI that truly understands and can accurately simulate our physical world has been a long-standing ambition. Sora's latest advancements bring us closer to this reality, challenging our perceptions of what's possible and opening doors to unprecedented innovation across various domains. Let's deconstruct Sora's technical edge: its profound progress in achieving temporal consistency and realistic physics simulation. For years, generating coherent, long-form video narratives with AI has been a critical challenge, often plagued by objects appearing and disappearing. Sora's ability to maintain continuity across frames marks a fundamental technical leap, moving beyond mere visual fidelity. Even more striking is the emergent capacity for Sora to understand and predict real-world dynamics. The demonstration of realistic physics within its outputs isn't just about pretty pictures; it suggests the model is developing a deeper comprehension of how objects interact, gravity behaves, and materials react. This goes beyond simple pattern recognition, hinting at an underlying 'world model.' This positions Sora not merely as a content creation tool, but as a potential platform for modeling complex physical interactions. Imagine high-fidelity engineering simulations, advanced robotics training, or accelerating scientific research by visualizing theoretical phenomena with unprecedented accuracy. These capabilities push the boundaries of AI's foundational understanding of our physical universe. What are the most significant implications of Sora's enhanced consistency and physics for your industry, and what new applications do you foresee emerging from these capabilities? #Sora #GenerativeAI #AIPhysics #VideoAI #Innovation #TechLeadership

🚀 Bringing #GenerativeAI and Reasoning to Computer Vision Pipelines NVIDIA just released version 2.4 of its Video Search and Summarization (VSS) Blueprint—and it’s a game changer for real-time video understanding. #computervision #ai #knowledgegraph

✨ Excited to share that Benchling is collaborating with NVIDIA! We’re bringing BioNeMo models directly into #BenchlingAI — so hundreds of thousands of researchers can get direct access to state-of-the-art models. It’s our vision for the future: #AI for every scientist, breakthroughs for all. https://lnkd.in/eJaM6qhB

MIT Lincoln Laboratory's newest supercomputer is the most powerful AI system at a U.S. university. Equipped for generative AI applications, TX-GAIN is supporting diverse research and development at the federally funded R&D center and across MIT.

Generative AI Transforms Materials Science With Breakthrough Designs Generative AI, long known for creating text, images, and code, is now being applied to an entirely different domain: the design of physical and scientific materials. Researchers are harnessing AI to accelerate breakthroughs in fields such as quantum technology, photonics, and advanced optics. From Creativity to Scientific Discovery Unlike consumer-facing uses of generative AI, materials #generativeAI #materialsscience #quantumtechnology

Generative AI Transforms Materials Science With Breakthrough Designs Generative AI, long known for creating text, images, and code, is now being applied to an entirely different domain: the design of physical and scientific materials. Researchers are harnessing AI to accelerate breakthroughs in fields such as quantum technology, photonics, and advanced optics. From Creativity to Scientific Discovery Unlike consumer-facing uses of generative AI, materials #generativeAI #materialsscience #quantumtechnology

Zeinteiq.ai has secured ₹75 crore from the IndiaAI Mission to develop a specialized 8-billion parameter, physics-aware scientific AI model. 🧠 By embedding the laws of physics, this model moves beyond standard generative AI to perform complex engineering simulations and predict real-world outcomes. This initiative is a clear signal of India's strategic focus on building foundational AI for core industrial R&D, aiming to accelerate innovation and reduce reliance on expensive physical prototypes. 🚀 #AI #IndiaAI #DeepTech #PhysicsAI #MakeInIndia #Engineering #Innovation #WeeklyVentures

"TX-GAIN will enable our researchers to achieve scientific and engineering breakthroughs. The system will play a large role in supporting generative AI, physical simulation, and data analysis across all research areas"   - Jeremy Kepner Love to see multi-modal #AI systems becoming more accessible to critical research. Well done #LLSC at Massachusetts Institute of Technology! https://lnkd.in/eV-sigzD

“Physics AI is just glorified curve fitting.” That’s what an engineer told me last week. Modern Physics AI models can map from boundary conditions and geometry to entire fields, so they’re clearly much more than simple regression. But I thought it’s actually a helpful mental model. These models do depend on training data, and extrapolation can fail. Yet that doesn’t change the fact that a good “curve fit” can be incredibly useful in engineering - especially when exploring large design spaces for high-value problems where brute-force simulation would not be economical. And the bar keeps moving. Model architectures are evolving: they generalize better, train faster, and are pushing the boundaries of where Physics AI is genuinely practical. But that isn’t the full picture of how AI impacts product development and simulation. A huge amount of time is still lost in manual hand-offs, waiting for sim tasks to bubble up in the backlog, setting up models, cleaning geometry, rerunning variations, and writing up results. None of this adds real engineering value, yet it eats up bandwidth. That’s where Engineering AI plays a role by automating the routine and standardizing best practices. Yesterday, together with Ian Pegler from NVIDIA and the crew at engineering.com, we showed both AI systems in action and did some live work on a cold plate design. Thanks to everyone who joined the session + find the recording below!

🚀 Breaking News in Computer Vision: GEYOLO-AHC is Here to Revolutionize Object Detection! 🚀 Imagine a world where your autonomous vehicles spot tiny obstacles in real-time, surveillance systems catch every occluded detail without missing a beat, and edge devices run lightning-fast detections that outperform the giants like YOLOv11. Sounds like sci-fi? Nope—it's the reality unlocked by GEYOLO-AHC: Graph-Enhanced YOLO with Adaptive Heat Conduction, our groundbreaking hybrid framework that's setting new benchmarks and redefining what's possible! As a passionate AI enthusiast, I'm thrilled to share this game-changing work. This is a masterful fusion of cutting-edge tech that bridges the best of convolutional efficiency, graph-based intelligence, and adaptive diffusion magic. 🔥 The Tech That Makes It Epic: Dynamic Graph Neural Network (GNN): Models object contours and spatial-semantic relationships like never before, turning cluttered scenes into crystal-clear detections. Say goodbye to missed small or occluded objects! Adaptive Heat Conduction Mechanism: Dynamically refines features with data-driven diffusion coefficients, grounded in anisotropic diffusion principles. It preserves edges, reduces noise, and adapts on-the-fly for unparalleled robustness. Attention-Enhanced Multi-Scale Feature Fusion: Handles objects of all sizes with an FPN that intelligently weighs scales via attention—optimizing for everything from tiny details to large structures. Built on YOLOv11's anchor-free backbone and CvHeat-DET's contour smarts, all unified under a mathematically proven graph-diffusion theory. This is principled innovation at its finest! 📈 Mind-Blowing Results That Prove It's a Beast: On COCO 2017: mAP@50:95 of 41.8% (beating YOLOv11's 40.5%, YOLOv10's 38.8%, YOLOv9's 37.5%, and CvHeat-DET's 39.5%) with blistering 12.5ms inference on NVIDIA A800—faster and smarter! On Pascal VOC 2007: Generalized mAP@50 of 78.2%, outshining YOLOv11 (75.6%) and CvHeat-DET (76.3%) by up to 2.6%. Especially shines on small objects with a 6.1% boost! Ablation studies confirm: The full synergy delivers simultaneous gains in accuracy, speed (80 FPS!), and efficiency (just 21.5M params). Hyperparameter-stable and edge-ready—no more trade-offs! This is a paradigm shift! GEYOLO-AHC tackles the toughest challenges in real-time detection, making it ideal for autonomous navigation, surveillance, edge computing, and multi-task vision systems. Whether you're building self-driving tech, security solutions, or next-gen apps, this model will supercharge your pipeline and give you that competitive edge. Ready to level up? Dive into the full paper here: https://lnkd.in/dKQ6kbup Code coming soon on GitHub—watch this space! Who's implementing this first? Drop your thoughts below. #ComputerVision #ObjectDetection #AI #YOLO #GraphNeuralNetworks #DeepLearning #Innovation #MachineLearning