What if someone could give our factories a conscience? A conscience powered by AI. 🧠💡 This is the SORDI.ai Sustainability-Agent preview, born from a collaboration between BMW Group, Google Cloud, and MONKEYWAY, and designed to be the central intelligence for sustainable operations. Its task is simple, yet its process is profound. When asked to "consider heat levels," it doesn't just read a sensor. It: 1️⃣ Perceives the entire environment through a live digital twin. 2️⃣ Understands the complex interplay between machinery, architecture, and nature. 3️⃣ Recommends an optimized, bio-integrated solution to cool the space efficiently. By embedding Google powered AI into the core of industrial operations via the SORDI.ai platform, it is creating facilities that can think, learn, and adapt to become truly sustainable. The future isn't just automated; it's autonomous and aware. #metavese #agentic #llm #robotics Andreas Adam Matthias Oehm Chafic ABOU AKAR Joe Khalil Hadi Koubeissy Benjamin Otto Nikolaos Ntemos, MBA NVIDIA Omniverse Rafael Gomez FDIA

By embedding Google powered AI into the core of industrial operations via the SORDI.ai platform, it is creating facilities that can think, learn, and adapt to become truly sustainable. The future isn't just automated; it's autonomous and aware. SORDI.ai Google Cloud #manufacturing #digitaltwin

Edge AI just jumped from crawling to sprinting. NVIDIA's Jetson AGX Thor now runs 70-billion parameter models that couldn't fit on previous generations. The difference? Two techniques most people haven't heard of. Quantization and speculative decoding changed everything. Quantization shrinks models without losing accuracy. It reduces precision to cut memory use. The result? 70B parameter models now fit on edge devices. Speculative decoding is even smarter. It uses a small "draft" model to predict tokens. The main model then verifies them. This boosted performance 2.5x in tests. The numbers tell the story: • 7x boost in generative AI throughput • Up to 2070 TFLOPS of FP4 compute • 128GB of high-bandwidth memory • Support for models like Llama 3.3 and DeepSeek R1 This isn't just about faster chips. It's about bringing advanced AI to robots, autonomous vehicles, and industrial systems. Edge AI used to mean simple tasks. Now it means running the same models as data centers. The gap between cloud and edge is shrinking fast. What changes when every device can think like GPT? #EdgeAI #GenerativeAI #NVIDIA 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/gyqZAFut

🤖 NVIDIA released Jetson Thor, a new edge-compute platform built to unlock real-time reasoning for physical AI.  What does this mean in practice? Robots will now be able to process multi-sensor data at lightning speed - making split-second decisions in dynamic, real-world environments. From humanoid robots like Agility Robotics’ Digit and Boston Dynamics’ Atlas, to surgical assistants, smart tractors 🚜, and even search-and-rescue bots 🚑 - Jetson Thor provides the kind of computing power that was previously reserved for server rooms, now available directly on-device. Read more: https://lnkd.in/dxYvqN2s  #Robotics #AI #Computing #PhysicalAI #Innovation VIDEO:  https://lnkd.in/dXjHp6BG 

Ever wondered what it looks like when an AI creates something? 🤔 Witness the process of our SORDI.ai generating a digital asset from scratch! 🤖✨ From a wireframe to a finished object, this is how artificial intelligence brings ideas to life. Getting into industrial #AI has never been easier! #metavese #agentic #llm #robotics Andreas Adam Matthias Oehm Chafic ABOU AKAR Joe Khalil Hadi Koubeissy Benjamin Otto Nikolaos Ntemos, MBA MONKEYWAY SORDI.ai BMW Group NVIDIA Omniverse Google Cloud Zac McLay Riley Mackay

From Weekend Flying to Cloud-Scale Thinking — Fixed-Wing Autonomy Meets GPU Simulation Last weekend’s “fun at the RC airfield” turned into something much bigger — a live experiment in AI-driven autonomy and GPU cloud simulation. I built and flight-tested a fixed-wing autonomous drone that flies without GPS, powered entirely by Visual SLAM and onboard AI inference running on a Jetson Orin. The aircraft perceives, maps, and self-corrects in real time — no satellite, no remote controller (I still have one just in case!) , just edge compute and algorithms doing their thing. But here’s the twist — all of the AI models, mapping logic, and flight profiles were trained, simulated, and stress-tested on Azure NC-series virtual GPUs (NVIDIA A10 v5) before deployment (If it was not a personal project I could do that on Parry's fully managed virtual work bench with GPU! which handles most of the provisioning automatically). That means I could run Isaac Sim and SLAM pipelines in a cloud GPU environment, validate flight scenarios, and push containerized models back down to the Jetson — a complete edge-to-cloud feedback loop from my home lab. 💡 Weekend Project Highlights: 🧠 Jetson Orin + TensorRT for onboard inference ☁️ Azure vGPU (A10 v5) for AI model training + simulation 🧭 Visual SLAM & AI perception stack (ROS 2 + Isaac SDK) 🚀 Fixed-wing platform tuned for GPS-denied flight autonomy 🔄 Real-time synchronization between cloud and physical flight tests This is kind of R&D — where edge devices meet GPU clouds, and a “weekend build” doubles as a live demo of scalable AI architecture. Projects like this remind me why I love working at the intersection of AI infrastructure, GPU systems, and autonomy — Thank you NVIDIA for building Jetson family of edge AI, Now we can build at global scale. #AIInfrastructure #EdgeAI #JetsonOrin #VisualSLAM #VirtualGPU #Azure #IsaacSim #NVIDIA #AutonomousSystems #WeekendEngineering #GPUComputing #MLOps #CloudArchitecture #PassionProject

Federated Learning for Edge Devices is reshaping how we think about AI in distributed environments. Rather than funneling massive datasets into the cloud, edge devices train locally and share only model updates. This keeps data private, reduces bandwidth usage, and yields models that reflect each device’s unique context. The approach is already powering smart agriculture, personal health, autonomous vehicles, and more. If you’re exploring ways to scale AI while staying compliant with data regulations, consider a federated learning roadmap. Check our company post for a deep dive into the architecture, tooling, and future directions. #FederatedLearning #EdgeAI #DataPrivacy #NeuraAI #meetneuraai #neuraai.

I’ve spent the last month researching how to bring AI models to edge devices without leaking data. Federated Learning for Edge Devices turns this vision into reality: each device trains locally and only shares tiny weight updates. It solves the biggest privacy concerns and cuts bandwidth costs dramatically. I think this approach will be key for industries like healthcare and agriculture where data is both sensitive and distributed. If you’re looking for a practical framework to start, check out our company post for a deep dive. #FederatedLearning #EdgeAI #Privacy #NeuraAI #meetneuraai #neuraai.

🚀 Alpha Binwani Capital: Nvidia’s Next Act Could Send Shares to $250 by 2026 Nvidia’s AI engine for the physical world just got a real-world boost. The chip giant has teamed up with Uber Technologies to push the frontier of autonomous driving and robotics AI training. 🔥 The catalyst? Uber’s massive real-world driving dataset — millions of daily trips that now feed directly into Nvidia’s Cosmos World Foundation Models. 🌌 Powered by DGX Cloud, the Cosmos platform trains AI to see, reason, and predict physical environments — a leap toward self-driving intelligence and digital-twin simulation. 🎯 The Cosmos Suite: • Cosmos‑Predict: Generates future frames and scenarios for world simulation • Cosmos‑Transfer: Enhances data diversity across real‑world conditions • Cosmos‑Reason: Vision‑language model that helps AI interpret physical interactions 📈 Financial punchline: Nvidia’s automotive and robotics segment exploded 72% YoY in Q1 FY2026 to $567M, after delivering $1.69B in FY2025 — the second straight year above $1B. 💬 Alpha Binwani Capital believes this growth curve, powered by Cosmos and Uber’s data, could propel NVDA to $250 by 2026. #Nvidia #Uber #AI #AutonomousDriving #Investing #Semiconductors #TechStocks #AlphaBinwaniCapital

🚀 AI is transforming industries—from patient care to enabling autonomous driving. NVIDIA NIM microservices simplify deploying powerful large language models, whether it's helping Mayo Clinic securely analyze sensitive health data 🏥 or enabling split-second decision-making for Waymo on the road 🚗. Need to keep data on-prem for privacy? Or scale in the cloud for performance? With NIM, you have the flexibility to do both. 💡 Check out Logan Lawler's latest blog to see how it works: 👉 https://lnkd.in/dVSz4kRE #iwork4dell #DellProMax #NVIDIA #AI #Healthcare #AutonomousVehicles #EdgeAI