Neuromorphic Computing: Revolutionizing AI with Brain-Inspired Technology

Quantum Computing & AGI: Catalysts for Transformation in Energy and Healthcare The pace of innovation in emerging technologies is accelerating, and two domains stand out for their transformative potential: Quantum Computing and Artificial General Intelligence (AGI). In the energy sector, quantum computing is redefining how we approach complex simulations—from optimizing grid performance and fuel efficiency to predictive maintenance and environmental modeling. These capabilities are not just theoretical—they’re paving the way for smarter, more sustainable operations. In healthcare, AGI introduces a paradigm shift. Beyond traditional AI, AGI systems can learn, reason, and adapt across diverse tasks. This opens doors to intelligent diagnostics, personalized treatment planning, and real-time decision support—ultimately enhancing patient care and operational agility. As these technologies mature, their convergence with enterprise platforms, IoT, and data analytics will unlock new possibilities. The challenge lies not just in adoption, but in aligning them with real-world needs, ethical frameworks, and scalable architectures. The future is not just digital—it’s intelligent, adaptive, and quantum-powered. #QuantumComputing #AGI #DigitalTransformation #EnergyInnovation #HealthcareTech #AI #EmergingTechnologies #SmartSystems #TechLeadership

🌐✨ Tech & Innovation Trends Shaping 2025 The world of technology is moving faster than ever, and here are some of the top shifts redefining industries right now: 🚀 Agentic AI – Moving beyond chatbots, AI agents can now book tickets, manage tasks, and operate autonomously. ⚡ Quantum Computing & Security – With 2025 marked as the International Year of Quantum Science, post-quantum cryptography is becoming critical. 🌱 Green Innovation – Sustainable IT, energy-efficient infrastructure, and eco-friendly data centers are shaping the future. 📶 Edge Computing & IoT – Smarter devices, real-time processing, and connected ecosystems are transforming homes, cities, and industries. 🩺 Deep-Tech Startups in India – From VR medical training to robotics and smart wearables, IIT Delhi’s FITT Forward 2025 is fueling a new era of innovation. 🏠 Smart Automation – Robotic arms, adaptive cleaning robots, and intelligent home systems are taking convenience to the next level. 🔮 The takeaway? Technology is not just evolving—it’s reshaping how we live, work, and connect. 👉 Which of these trends excites you the most? #Innovation #AI #QuantumComputing #Sustainability #TechTrends2025 #FutureOfWork

Latest News from Voxstar - Google is accelerating scientific breakthroughs with an AI co-scientist—a multi-agent system powered by Gemini 2.0. This virtual collaborator helps researchers generate novel hypotheses and advance research proposals, bridging human expertise with powerful AI insights. Discover how this partnership is driving innovation across industries. [ [Visit Voxstar.com](https://voxstar.com) #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #ComputerVision #AI #DataScience #NaturalLanguageProcessing #BigData #Robotics #Automation #IntelligentSystems #CognitiveComputing #SmartTechnology #Analytics #Innovation #Industry40 #FutureTech #QuantumComputing #Iot #blog #x #twitter #genedarocha #voxstar #wiredvibeapp #audiocaster.ai @ArtificialIntelligence @MachineLearning @DeepLearning @NeuralNetworks @ComputerVision @AI @DataScience @NaturalLanguageProcessing @BigData @Robotics @Automation @IntelligentSystems @CognitiveComputing @SmartTechnology @Analytics @Innovation @Industry40 @FutureTech @QuantumComputing @Iot @blog @x @twitter @genedarocha @voxstar #audiocaster.ai

🚀 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗯𝗲𝘆𝗼𝗻𝗱 𝗰𝗼𝗱𝗲 𝗮𝗻𝗱 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗳𝗮𝗯𝗿𝗶𝗰 𝗼𝗳 𝗿𝗲𝗮𝗹𝗶𝘁𝘆. Two 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝘀 caught my eye this week: 🔹 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (QML) for Semiconductors Researchers have used a QML model called Quantum Kernel-Aligned Regressor (QKAR) to design semiconductors up to 20% more efficiently than classical ML. Imagine ML models not just running on chips—but actually helping to create the next generation of them. 🤯 👉 https://lnkd.in/dsaqpPvs? 🔹 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 in AI We’re moving past purely digital learning toward AI that adapts to the physical world. Think drones navigating unpredictable environments or robotics infused with physics-informed ML. This shift from “data intelligence” to “physical intelligence” could redefine how machines coexist with us. 👉 https://lnkd.in/dZKeCmBR? As an ML engineer, I find both directions exciting: QML could optimize edge devices, IoT, and chip pipelines. Physical intelligence could unlock safer, more reliable AI in robotics and real-world systems. 💡 I’d love to know: Where do you see the bigger impact—quantum ML shaping chips, or physical intelligence shaping robotics? #MachineLearning #QuantumML #PhysicalIntelligence #Innovation #MLinProduction

🌍 The Future of IT: Emerging Tech to Watch in 2025 🚀 Technology isn’t slowing down — it’s accelerating. Here are 3 game-changing trends shaping IT in 2025: 🔹 Quantum Computing – Moving beyond theory, quantum breakthroughs are tackling real-world problems, such as drug discovery, climate modeling, and financial risk analysis. It’s no longer “if” but “when.” 🔹 Edge Computing & IoT – With billions of devices generating data, pushing computation closer to the source reduces latency and enables real-time decisions. From autonomous vehicles to healthcare monitoring, edge is becoming mainstream. 🔹 Green IT & Sustainable Tech – Data centers are responsible for ~2% of global electricity use. In 2025, companies are doubling down on energy-efficient chips, carbon-neutral cloud services, and green AI models. 💡 My Take: The next wave of IT innovation will not only be about speed and power — it will be about responsibility, sustainability, and accessibility. 👉 What do you think? Which of these trends will have the biggest impact on our future? #EmergingTech #FutureOfWork #QuantumComputing #EdgeComputing #GreenIT #AI

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

As AI adoption skyrockets, businesses are shifting from traditional cloud-based AI to Edge AI—where intelligence is deployed closer to data sources like IoT devices, sensors, and autonomous systems. This shift is redefining real-time computing, security, and efficiency across industries. 🔹 What is Edge AI? Edge AI combines Artificial Intelligence (AI) and Edge Computing, allowing devices to process and analyze data locally rather than sending it to centralized cloud servers. This enables instant decision-making with minimal latency and higher security. 🔹 Why is Edge AI Transformational? ✅ Real-Time Processing ⚡ – Critical applications like self-driving cars 🚗, industrial automation 🏭, and healthcare diagnostics 🏥 require ultra-fast AI decisions. ✅ Lower Latency ⏳ – By reducing dependency on cloud-based processing, Edge AI ensures faster responses for mission-critical applications. ✅ Enhanced Security & Privacy 🔒 – Sensitive data is processed on-device, minimizing cybersecurity risks and compliance concerns. ✅ Optimized Bandwidth Usage 📡 – Less data transmission means lower cloud storage costs and improved network efficiency. ✅ Scalability for IoT & 5G 🌎 – Edge AI accelerates the growth of smart cities, connected devices, and intelligent manufacturing. 🔹 Where is Edge AI Making an Impact? 🚗 Autonomous Vehicles – AI-powered real-time navigation & obstacle detection 🏥 Healthcare – Faster medical diagnostics & personalized treatments 🏭 Manufacturing – AI-driven predictive maintenance & process optimization 📱 Smartphones & Wearables – AI-enhanced user experiences with voice assistants & health tracking 🎥 Surveillance & Security – AI-based facial recognition & threat detection 🌍 Edge AI is the Future! Are You Ready? The world is rapidly moving toward a hyperconnected future powered by AI at the edge. Companies investing in AI chips, embedded AI models, and IoT-powered ecosystems will gain a competitive advantage. 💡 What are your thoughts on the future of Edge AI? How do you see it transforming industries? Let’s discuss in the comments! 👇 #EdgeAI #ArtificialIntelligence #AITrends #MachineLearning #IoT #AIInnovation #FutureOfTech #CloudComputing #5G

What if your equipment could tell you it's about to fail? 👂 At Akhila Labs, we've pioneered an Audio AI solution that listens for the subtle sounds of machine wear and tear, predicting failure with remarkable accuracy. This isn't science fiction—it's the future of predictive maintenance. Imagine significantly reducing unplanned downtime and slashing maintenance costs... We're making it a reality with our Smart Predictive Maintenance system, designed for loud, industrial environments. Here’s a glimpse into the tech that makes it happen: Custom One-Class AI Model: Trained to identify "normal" machine sounds, instantly flagging any deviations as anomalies. 🔊 Advanced DSP Integration: Utilizes Digital Signal Processing to filter out noisy backgrounds and extract crucial audio features for the AI. 📊 RTOS Firmware: Built on a Real-Time Operating System for deterministic, low-latency audio sampling and processing, ensuring reliability. ⏱️ Ultra-Low Power Consumption: Operates on minimal power, making it ideal for long-term, battery-powered deployment in remote locations. 🔋 The impact? Predicting failures with 95% accuracy and reducing unplanned downtime by 50%! Ready to transform your maintenance strategy from reactive to truly predictive? 👉 Let's connect and explore how our Audio AI can bring unparalleled efficiency to your operations. #AudioAI #EmbeddedSystems #PredictiveMaintenance #IIoT #AI #SmartManufacturing #Industry40 #AkhilaLabs #Innovation #IoT

We are beyond the point where 'AI at the edge' is just a buzzword. It’s changing how we build and manage connected systems. ICYMI: Here are some highlights from SORACOM CTO & co-founder Kenta Yasukawa on the Embedded Insiders Podcast (Embedded Computing Design): 🔹 𝘼𝙄 𝙞𝙣 𝙄𝙤𝙏 𝙞𝙨 𝙝𝙚𝙧𝙚 𝙣𝙤𝙬 → With Soracom Flux, developers can connect devices, sensors, and AI models using natural language. No deep coding required. 🔹 𝘿𝙖𝙩𝙖 𝙞𝙣𝙨𝙞𝙜𝙝𝙩𝙨 𝙢𝙖𝙙𝙚 𝙨𝙞𝙢𝙥𝙡𝙚 → Soracom Query lets users ask questions in plain English and get instant reports, making device and network management accessible to everyone. 🔹 𝙍𝙚𝙖𝙡-𝙬𝙤𝙧𝙡𝙙 𝙞𝙢𝙥𝙖𝙘𝙩 → From warehouse security to reducing food waste in retail, AI + IoT is already driving measurable results. 🔹 𝙏𝙝𝙚 𝙨𝙝𝙞𝙛𝙩 𝙞𝙣 𝙥𝙤𝙬𝙚𝙧→ Domain experts (store managers, ops teams, engineers) can now directly shape AI-driven applications without needing a full dev team. 🔹𝙎𝙪𝙨𝙩𝙖𝙞𝙣𝙖𝙗𝙡𝙚 𝙨𝙘𝙖𝙡𝙞𝙣𝙜 → Start with large AI models in the cloud, then optimize with smaller edge models for cost and energy efficiency. 👉 The future of IoT and AI is about lowering barriers so anyone can innovate. 🎧 Listen here: https://bit.ly/45w62k4 #genAI #aiiot #ai #iotsolution #AIontheedge

When considering the balance between edge AI and cloud-side LLM, the answer often comes down to a familiar refrain that I used to use as a solutions architect a lot in my career: "it depends." I'm not kidding or trying to dodge the answer because navigating this question requires a nuanced approach tailored to the specifics of each system and use case. In scenarios where an edge device boasts robust capabilities, remains consistently powered, and handles significant data loads, leveraging edge processing may prove advantageous. Conversely, when working with numerous microcontrollers and analyzign aggregated data is important, cloud-based processing emerges as the more suitable choice. My current recommendation revolves around aggregating data comprehensively and utilizing cutting-edge AI models in the cloud to unlock the full potential of data collected from your devices and what actions you can take with your devices. By analyzing that with the latest and great AI, you can see the maximum value you can get from your connected devices. Then, you can consider optimizing the value-to-cost ratio, a strategic balance between edge and cloud solutions, and so on. If you are interested in the topic, I talked about it in the latest episode of the Embedded Insiders podcast, hosted by Kenneth Briodagh on Embedded Computing Design . Tune in to hear more about it!. https://lnkd.in/eJuv2BSc #AI #GenAI #EdgeComputing #CloudTechnology #EmbeddedSystems #TechDiscussion #IoT