Catharina Yu Berger’s Post

A great deep dive into how NVIDIA and Microsoft are turning Physical AI into reality, from models to intelligent machines at production scale. A must-watch if you’re thinking about how to move from vision to execution in Physical AI - and where to start. #PhysicalAI #Robotics #NVIDIA #Microsoft #Azure #DigitalTwin #IndustrialTransformation #Omniverse #OpenUSD

On this week of HANNOVER MESSE, it is the perfect time to revisit how Microsoft and NVIDIA are collaborating to make the future of #manufacturing a reality!

🚀 AI is exploding with efficiency breakthroughs and industrial integrations this week! Here's the pulse on the hottest developments: 🤖 **Google's TurboQuant slashes memory overhead** for large AI models via a two-step compression algo, enabling massive context windows to run efficiently. (via Crescendo.ai – https://lnkd.in/ek8-FxpY) 🚀 **SAP partners with ANYbotics for physical AI in industry**, integrating four-legged robots with SAP's ERP for seamless factory automation. (via ArtificialIntelligence-News – https://lnkd.in/ebHm5Niu) 💰 **ScaleOps raises $130M to optimize computing**, tackling efficiency as demand skyrockets for training massive models. (via TechCrunch – https://lnkd.in/eyiZE392) 🧬 **Eli Lilly launches LillyPod supercomputer** with 1,016 NVIDIA GPUs, simulating billions of molecules to halve drug development time. (via Crescendo.ai – https://lnkd.in/ek8-FxpY) ⚙️ **MIT AI designs proteins by motion**, unlocking dynamic biomaterials and adaptive therapeutics. (via MIT News – https://lnkd.in/eRYHk7Zy) Ready to automate smarter? Task Driven Solutions offers **Custom Automations**, streamlining unique business needs and boosting efficiency. Check them out: https://lnkd.in/eszyKN2R For private AI, try Venice AI: 100% local processing keeps prompts on-device, powered by uncensored open-source models. https://lnkd.in/ecJg3sfQ #AI #ArtificialIntelligence #MachineLearning #GenerativeAI #AIEfficiency #AIinIndustry #DeepLearning #AIFunding

🤖 AI Isn’t Just Chatbots—It’s an Infrastructure Race ⚡🌍 Jensen Huang explains that AI isn’t just about models or apps 🤯—it’s a full stack built layer by layer It starts from the ground up 👇 ⚡ Energy, data centers, cooling 💻 GPUs and chips for computation 🌐 Infrastructure & software to scale systems 🧠 AI models 📱 Final applications users interact with Only when the lower layers are strong… the top layers can succeed 🚀 Huang also highlighted China 🇨🇳—pointing out how fast it builds large-scale infrastructure compared to the US ⚡ The real insight? 💡 AI leadership isn’t just about smarter models—it’s about how fast you can build the foundation Because if the base layers move faster… everything above them accelerates too 🔥 AI isn’t just a tech race—it’s an infrastructure race 🌍 What do you think? 🤔💬 Read Full Article Link in Bio @musicyricsmedia & media.musicyrics.com #AI #ArtificialIntelligence #JensenHuang #NVIDIA #TechNews #FutureTech #Innovation #DataCenters #Infrastructure #GlobalTech

AI at the edge just got smarter, faster, and far more practical. Liquid AI has released LFM2.5-VL-450M, a compact vision-language model built for real-world deployment on edge hardware. 🚀 This updated model brings powerful capabilities into a tiny 450M-parameter footprint, making it highly relevant for developers, product teams, and AI builders who need speed without sacrificing utility. ⚙️ Key benefits include: • Bounding box prediction for vision tasks and object localization 📦 • Improved instruction following for more reliable responses 🧠 • Expanded multilingual understanding for global applications 🌍 • Function calling support for workflow automation and agentic use cases 🔧 • Sub-250ms inference designed for edge devices like NVIDIA Jetson Orin and mini PCs ⚡ Why this matters: This is a strong step toward practical AI that can run closer to the user, reduce latency, improve privacy, and unlock smarter on-device experiences. 📱 For tech professionals, this opens exciting opportunities in robotics, retail analytics, industrial inspection, smart assistants, and multilingual edge applications. 🔍 What do you think will drive the next big wave in edge AI: better multimodal reasoning, faster inference, or more efficient deployment? Share your thoughts below and let’s discuss where compact vision-language models fit in your roadmap. 🤝 Follow our community for more high-impact AI updates, model releases, and edge computing insights that help you stay ahead. ✅ #EdgeAI #VisionLanguageModels #MultimodalAI #GenerativeAI #AIML #OnDeviceAI #TechNews Lets Connect 🤝 ♻️ Repost, 👍 like and ✅ follow me on 🆇 for more insightful updates on AI

AI in hardware is finally hitting its stride. For years it felt like a buzzword - thrown at every firmware update and sensor optimization. But something shifted. The constraints of physical systems actually make AI better, not worse. You can't iterate on hardware like software. No weekly deploys, no quick rollbacks. So you build smarter from the start - prediction systems that catch failures before they happen, anomaly detection that learns your specific device instead of generic patterns. The cost of getting it wrong forces you to think differently. I've been digging into this space and the engineering is solid. Real time inference on edge devices, minimal power overhead, systems that gracefully degrade instead of catastrophically failing. It's the opposite of over-engineered - it's lean, purposeful design. The best part? Hardware people are finally taking AI seriously instead of bolting it on as an afterthought. When you have to ship something physical, you don't waste time with hype. You solve actual problems. What hardware problems are you seeing AI actually move the needle on? #Hardware #AI #Engineering #EdgeComputing

AI Infrastructure & Why Every Sector Depends On It ⚙️ Every industry talking about AI is quietly dependent on the same thing: infrastructure that can actually handle it. This was the insight that sent me down the rabbit hole of building the AI infrastructure course. It's not just a tech problem. It's a business problem — and it's showing up differently across every sector. Here's what's driving real GPU cluster demand right now: Healthcare — training models on medical imaging, genomics, and patient records requires serious compute. Not occasional. Continuous. Finance — fraud detection and real-time risk inference running across millions of events simultaneously, every second the market is open. Manufacturing — computer vision on production lines, predictive maintenance on equipment, digital twin simulations that mirror physical operations in real time. Energy — grid optimization, demand forecasting, and climate modeling at scales that simply weren't possible five years ago. Logistics — route optimization, warehouse robotics, and supply chain simulation that never stops running regardless of time zone or season. Each of these sectors has completely different infrastructure requirements. Different storage profiles. Different networking needs. Different operational demands. Understanding that isn't just useful for engineers anymore. It's becoming the kind of literacy that separates leaders who make smart AI investments from those who make expensive mistakes. Still building my own understanding of this. Sharing because I think more people should be asking these questions. #AIInfrastructure #EnterpriseAI #GPUComputing #MLOps #DigitalTransformation #TechCareers #LearnInPublic #BuildInPublic

AI Infrastructure & Why Every Sector Depends On It ⚙️ Every industry talking about AI is quietly dependent on the same thing: infrastructure that can actually handle it. This was the insight that sent me down the rabbit hole of building the AI infrastructure course. It's not just a tech problem. It's a business problem — and it's showing up differently across every sector. Here's what's driving real GPU cluster demand right now: Healthcare — training models on medical imaging, genomics, and patient records requires serious compute. Not occasional. Continuous. Finance — fraud detection and real-time risk inference running across millions of events simultaneously, every second the market is open. Manufacturing — computer vision on production lines, predictive maintenance on equipment, digital twin simulations that mirror physical operations in real time. Energy — grid optimization, demand forecasting, and climate modeling at scales that simply weren't possible five years ago. Logistics — route optimization, warehouse robotics, and supply chain simulation that never stops running regardless of time zone or season. Each of these sectors has completely different infrastructure requirements. Different storage profiles. Different networking needs. Different operational demands. Understanding that isn't just useful for engineers anymore. It's becoming the kind of literacy that separates leaders who make smart AI investments from those who make expensive mistakes. Still building my own understanding of this. Sharing because I think more people should be asking these questions. #AIInfrastructure #EnterpriseAI #GPUComputing #MLOps #DigitalTransformation #TechCareers #LearnInPublic #BuildInPublic

AI Infrastructure & Why Every Sector Depends On It ⚙️ Every industry talking about AI is quietly dependent on the same thing: infrastructure that can actually handle it. This was the insight that sent me down the rabbit hole of building the AI infrastructure course. It's not just a tech problem. It's a business problem — and it's showing up differently across every sector. Here's what's driving real GPU cluster demand right now: Healthcare — training models on medical imaging, genomics, and patient records requires serious compute. Not occasional. Continuous. Finance — fraud detection and real-time risk inference running across millions of events simultaneously, every second the market is open. Manufacturing — computer vision on production lines, predictive maintenance on equipment, digital twin simulations that mirror physical operations in real time. Energy — grid optimization, demand forecasting, and climate modeling at scales that simply weren't possible five years ago. Logistics — route optimization, warehouse robotics, and supply chain simulation that never stops running regardless of time zone or season. Each of these sectors has completely different infrastructure requirements. Different storage profiles. Different networking needs. Different operational demands. Understanding that isn't just useful for engineers anymore. It's becoming the kind of literacy that separates leaders who make smart AI investments from those who make expensive mistakes. Still building my own understanding of this. Sharing because I think more people should be asking these questions. #AIInfrastructure #EnterpriseAI #GPUComputing #MLOps #DigitalTransformation #TechCareers #LearnInPublic #BuildInPublic

AI Infrastructure & Why Every Sector Depends On It ⚙️ Every industry talking about AI is quietly dependent on the same thing: infrastructure that can actually handle it. This was the insight that sent me down the rabbit hole of building the AI infrastructure course. It's not just a tech problem. It's a business problem — and it's showing up differently across every sector. Here's what's driving real GPU cluster demand right now: Healthcare — training models on medical imaging, genomics, and patient records requires serious compute. Not occasional. Continuous. Finance — fraud detection and real-time risk inference running across millions of events simultaneously, every second the market is open. Manufacturing — computer vision on production lines, predictive maintenance on equipment, digital twin simulations that mirror physical operations in real time. Energy — grid optimization, demand forecasting, and climate modeling at scales that simply weren't possible five years ago. Logistics — route optimization, warehouse robotics, and supply chain simulation that never stops running regardless of time zone or season. Each of these sectors has completely different infrastructure requirements. Different storage profiles. Different networking needs. Different operational demands. Understanding that isn't just useful for engineers anymore. It's becoming the kind of literacy that separates leaders who make smart AI investments from those who make expensive mistakes. Still building my own understanding of this. Sharing because I think more people should be asking these questions. #AIInfrastructure #EnterpriseAI #GPUComputing #MLOps #DigitalTransformation #TechCareers #LearnInPublic #BuildInPublic