Understanding the AI Spectrum: From Cloud to Battery-less Endpoints

In AI, your use case decides your winner.   Pick the wrong LLM and you’re burning cash.   This MMLU benchmark makes one thing clear, performance gaps between top models are widening. But raw scores don’t tell the whole story.   What matters is your business case: • In medical diagnostics, accuracy isn’t optional, one wrong output could mean a wrong diagnosis. • In industrial IoT, you need a model that can process massive sensor data streams in real time without choking budgets. • In finance, compliance and explainability can matter more than speed. • For deployment on edge or across public/private clouds, you need to match model performance with latency, security, and cost constraints.   The best model for one industry could be a costly mistake in another.   Model selection isn’t just a technical choice. It’s a business decision that defines ROI, customer safety, and your ability to scale.   The race isn’t about “using AI” anymore. It’s about picking the right intelligence to partner with.   DM to discuss the best LLM for your business use case.   Image source: https://lnkd.in/dvrPq4fG   #LLMs #ArtificialIntelligence #MMLU #AIModels #AIAdoption

🚀 Edge AI + IoT = The Manufacturing Revolution of 2025 🚀 By 2025, edge AI is no longer a “nice‑to‑have” but a must‑have for IoT‑enabled factories. Instead of sending terabytes of raw sensor data to the cloud, devices now process, learn, and act locally—right where the action happens. Why it matters - ⏱️ Real-time analysis: Decisions made in milliseconds, eliminating lag that once forced human intervention. - 🔧 Predictive maintenance: Sensors detect anomalies pre-failure, cutting downtime by ~30% & extending equipment lifespan. - 💸 Bandwidth & cost savings: Only essential insights travel to the cloud, reducing data expenses & freeing network capacity. - 🔒 Data privacy: Sensitive data never leaves the plant, meeting strict compliance standards. - ↗️ Scalable automation: Add/upgrade edge nodes without re-architecting entire systems. What’s driving adoption? - 🌐 5G & low-latency networks eliminate cloud bottlenecks for edge nodes. - 🔌 Miniaturized AI chips (e.g., NVIDIA Jetson, Intel Movidius) pack powerful inference into compact devices. - 📦 Standardized frameworks (ONNX, TensorRT, EdgeX Foundry) simplify development for engineers. - ☁️ Hybrid cloud-edge pipelines keep strategic data centralized while running latency-critical tasks locally. Real-world snapshot A leading automotive supplier integrated edge AI into paint-shop robots: ✅ 25% fewer paint defects ✅ 15% higher throughput ✅ 10% energy reduction Next steps for your organization 1. 🔍 Audit: Identify data-heavy processes needing real-time optimization. 2. 🛠️ Pilot: Deploy edge AI on a single machine or sensor cluster. 3. 🚀 Scale: Expand across facilities using hybrid architecture for analytics & compliance. Edge AI isn’t tomorrow’s buzzword—it’s today’s powerhouse for factories demanding speed, safety & efficiency. If you’re still depending on cloud-only models, 2025 is your pivot year. 🔗 Let’s discuss strategically deploying edge AI in your operations. #EdgeAI #IoT #Manufacturing #Industry40 #DigitalTransformation #AI #SmartManufacturing #RealTimeAnalytics #PredictiveMaintenance #5G #Innovation #FutureOfWork #Automation ---

🏭 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 is 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 operations on a 𝗱𝗲𝗲𝗽 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗹𝗲𝘃𝗲𝗹, 𝗳𝗮𝗿 𝗯𝗲𝘆𝗼𝗻𝗱 casual media coverage. According to the 𝟮𝟬𝟮𝟱 𝗔𝗜 𝗜𝗻𝗱𝗲𝘅 𝗥𝗲𝗽𝗼𝗿𝘁 by 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗛𝗔𝗜 and the systematic review by Rashid et al. (2024), AI applications now dominate strategic functions such as automated inspection through optimized convolutional neural networks and predictive maintenance powered by advanced IoT sensors. This integration relies on complex edge computing systems processing real-time data for immediate decision-making. ➡️ 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗲𝗺𝗽𝗹𝗼𝘆𝘀 𝗖𝗡𝗡𝘀 achieving over 99% defect detection accuracy, outperforming classical methods. ➡️ 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲𝘀 𝗺𝘂𝗹𝘁𝗶𝘃𝗮𝗿𝗶𝗮𝘁𝗲 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 models, including XGBoost and LSTMs, predicting failures ahead of time to reduce downtime and costs. ➡️ 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗰𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗿𝗲𝗹𝗶𝗲𝘀 𝗼𝗻 𝘂𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗮𝗻𝗼𝗺𝗮𝗹𝘆 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 algorithms protecting SCADA and OT systems in critical infrastructure environments. This adoption builds 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, where 𝗔𝗜 𝗮𝗻𝗱 𝗜𝗼𝗧 𝗼𝗽𝗲𝗿𝗮𝘁𝗲 𝘀𝘆𝗻𝗲𝗿𝗴𝗶𝘀𝘁𝗶𝗰𝗮𝗹𝗹𝘆, supported by 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝟱𝗚 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 guaranteeing minimal latency. Is your enterprise ready for this technical transformation? #IndustrialAI #SmartManufacturing #ComputerVision #PredictiveMaintenance #IndustrialCybersecurity #EdgeComputing #IoT #5GNetworks #DataScience

The Incredible Shrinking AI: A Game-Changer for IoT Researchers have developed an AI-powered camera that's roughly the size of a coarse grain of salt. This isn't just a novelty; it's a monumental leap for Edge AI. This tiny camera uses a neural network to process images directly on the device, without needing to send data to the cloud. For us as embedded engineers, the implications are massive: 🔹 Enhanced Privacy: Sensitive data (like in medical devices) can be processed locally, drastically reducing security risks. 🔹 Extreme Power Efficiency: It operates on minuscule power, opening doors for long-lasting smart sensors and autonomous devices. 🔹 New Possibilities: Imagine smart dust that can monitor crop health or microscopic robots for non-invasive surgery. This is the very essence of Edge AI – bringing intelligence to the source. The demand for engineers who can build, optimize, and deploy these tiny, powerful systems is only going to grow. The future is not just smart; it's invisibly intelligent. What applications for this technology excite you the most? #EdgeAI #IoT #EmbeddedSystems #Tech #Innovation #FutureOfTech #AI #Engineering #ECE #LinkedInForEngineers

Edge AI vs Cloud AI, Trade-offs, Challenges, and the Future Every AI request, whether recognizing an image, translating speech, or running a recommendation, faces a key question: where should it be processed? Cloud AI: High computational power Low latency isn’t guaranteed, especially for real-time tasks Energy-intensive Data leaves your device → privacy concerns Edge AI: Computation happens directly on your device Real-time responses, critical for self-driving cars, AR/VR, and IoT sensors Energy-efficient, avoids sending huge amounts of data to the cloud Better privacy, data stays local But Edge AI has limitations: Smaller memory and compute capacity Harder to run massive models. Hardware varies across devices → optimization is complex This raises a big question for the AI community: Will the future of Edge AI rely on optimizing models to run efficiently on small devices, or will we keep designing new, specialized chips to handle bigger models at the edge? This reel visualizes cloud vs edge processing, highlighting latency, energy, and privacy differences. I’d love to hear your thoughts, model optimization vs hardware innovation: which path will dominate? and to what extent do you think we can optimize? Subscribe to My Channel for more: https://lnkd.in/dUFv9DBu #EdgeAI #CloudAI #AIHardware #MachineLearning #DeepLearning #NeuralNetworks #IoT #AIOptimization #RealTimeAI #DataPrivacy #AIExplained #FutureOfAI #HardwareAwareAI

Edge AI: Bringing Intelligence Closer to the Data Source Edge AI is revolutionizing how we process and utilize data, moving artificial intelligence capabilities from centralized cloud servers to the very devices where data is generated. This paradigm shift offers significant benefits, particularly in terms of reduced latency, enhanced privacy, and a myriad of applications across various sectors. By processing data locally on edge devices – such as IoT sensors, smartphones, and industrial equipment – Edge AI minimizes the time it takes for insights to be generated and actions to be taken. This low latency is crucial for real-time applications like autonomous vehicles, industrial automation, and critical infrastructure monitoring, where milliseconds can make a difference. Furthermore, Edge AI significantly enhances data privacy and security. Instead of transmitting sensitive data to the cloud for processing, computations are performed on the device itself. This reduces the risk of data breaches and ensures that personal or proprietary information remains localized, addressing growing concerns around data governance and compliance. The applications of Edge AI are vast and rapidly expanding. In smart cities, it enables intelligent traffic management systems and predictive maintenance for public utilities. In healthcare, it powers wearable devices for real-time health monitoring. For manufacturing, it facilitates predictive analytics on factory floors, optimizing operations and preventing downtime. Edge AI is not just a technological advancement; it's a fundamental shift towards a more responsive, secure, and intelligent connected world. #EdgeAI #AI #IoT #SmartCities #Privacy #Latency #ArtificialIntelligence #Innovation #Technology

How small can powerful AI get? Multiverse Computing just shattered expectations with SuperFly and ChickBrain—AI models so compact, they’re named after a fly’s and a chicken’s brain, yet they’re set to transform how we interact with technology at the edge. Imagine running advanced chat, speech, and reasoning directly on your phone, laptop, or even your washing machine—no cloud, no lag, just instant intelligence. That’s the promise of Multiverse’s new models, designed for edge devices and IoT, and powered by their quantum-inspired CompactifAI compression tech. Key Takeaways: • Superfly: Just 94 million parameters, yet robust enough for real-time voice commands in home appliances and smart devices. • @ChickBrain 3.2 billion parameters, outperforming its original on key benchmarks, and can run locally on laptops—no internet required. • CompactifAI: Multiverse’s proprietary tech shrinks models dramatically without sacrificing performance, making AI more accessible and private. • Backed by $215M in funding and partnerships with HP, Toshiba, and more, Multiverse is already in talks with Apple, Samsung, and Sony to bring these models to everyday devices. • Model Zoo: A growing library of edge-ready AI models, opening up new possibilities for smart, responsive, and secure AI everywhere. Why does this matter now? • Edge AI is exploding as privacy, speed, and offline capability become must-haves for consumers and businesses. • Smaller, high-performing models mean AI can be embedded in everything from wearables to industrial sensors, unlocking new use cases and business models. • Quantum-inspired compression could be a game-changer for the entire industry, making advanced AI affordable and sustainable at scale. What’s next? • Will ultra-compact AI models redefine what’s possible for smart devices and the Internet of Things? • How will this shift impact privacy, user experience, and the future of AI-powered products? Let’s discuss: What’s the most exciting application you see for edge AI in your world? Where do you think ultra-compact models will have the biggest impact? #AI #EdgeAI #IoT #ModelCompression #QuantumTech #MultiverseComputing #GenAI #TechStartups #VoiceAI #SmartDevices

🚀 AI-Powered Smart Surveillance on Raspberry Pi 5 with Arm Compute Library & ONNX Runtime I’m thrilled to share how the combination of Raspberry Pi 5 + Arm Compute Library + ONNX Runtime is redefining what’s possible in edge AI surveillance. 🔹 With Raspberry Pi 5’s enhanced CPU/GPU performance, we can now run real-time computer vision models locally — no dependency on cloud latency. 🔹 The Arm Compute Library optimizes neural network inference directly on ARM hardware, ensuring low power, high efficiency, and scalability. 🔹 Paired with ONNX Runtime, it unlocks the ability to deploy state-of-the-art AI models seamlessly at the edge, making it easier to adapt surveillance systems for object detection, anomaly tracking, and smart alerts. What excites me most is how this empowers developers, researchers, and makers to build secure, private, and intelligent surveillance systems without heavy infrastructure. From smart cities to enterprise security to home IoT projects, the potential is enormous. 💡 This is just the beginning — the edge AI ecosystem is growing fast, and ARM-powered devices are at the heart of it. 👉 Would you like me to share a step-by-step demo of how to set up smart surveillance with Raspberry Pi 5 + ARM Compute Library + ONNX Runtime? #ARM #Ambassador #AI #EdgeAI #RaspberryPi5 #ONNXRuntime #SmartSurveillance #IoT #ComputerVision

This week, we had hundreds of engineers join us live to learn all about implementing ML in their designs. Next up, we’ll show you how to take existing AI models and make them perform at their best for your application. Whether you are working with vision, audio, or other sensor-based tasks, you’ll see how retraining, transfer learning, and synthetic data generation can unlock higher accuracy without starting from scratch. You’ll learn: • Why off-the-shelf models often fail in real-world embedded use cases • How to collect and prepare application-specific datasets • Using synthetic data to close coverage gaps and boost robustness • Deploying optimised, quantised models to Alif’s Ensemble and Balletto MCUs • Practical steps to evaluate and refine accuracy before deployment This is a hands-on, engineer-focused guide using proven workflows on the Edge Impulse (a Qualcomm company) platform with Alif Semiconductor’s fusion processors. 👉 Sign up here to secure your spot: https://lnkd.in/eikbb_kE #EmbeddedAI #EdgeAI #MachineLearning #AIoT #IoT #MCU #ModelTraining #SyntheticData #TransferLearning #Engineers

AI gets all the headlines. But without embedded computing, it’s just a brilliant idea with nowhere to run. In the race to build smarter cities, safer factories, and more responsive healthcare systems, AI is the brain—but embedded computing is the nervous system. It’s what puts intelligence into motion, at the edge, in real time. Think of it like this: AI says, “I know what to do.” Embedded computing says, “I’ll do it—right here, right now.” From sensors that detect anomalies in milliseconds to edge devices that make split-second decisions without cloud latency, embedded systems are the quiet enablers of intelligent action. Whether it’s a rugged gateway in a remote oil field or a tiny module inside a wearable device, embedded computing brings AI to life where it matters most: close to the data, close to the problem, close to the people. As someone who’s spent years in cloud and now dives deep into embedded, I’m seeing firsthand how this convergence is reshaping industries. It’s not just about performance—it’s about empathy. Solving real-world problems with real-time intelligence. Let’s give embedded its moment. Because AI can’t work without it. #EmbeddedComputing #AI #EdgeIntelligence #IoT #TechWithHeart #Advantech #EmpathyInTech