estigiti’s Post

𝐌𝐲 𝐣𝐨𝐮𝐫𝐧𝐞𝐲 𝐢𝐧𝐭𝐨 𝐀𝐈 𝐛𝐞𝐠𝐚𝐧 𝐚𝐭 𝐭𝐡𝐞 𝐞𝐝𝐠𝐞.⁣⁣ ⁣⁣ When I started working in AI at eInfochips, my very first project was to 𝐝𝐞𝐩𝐥𝐨𝐲 𝐚 𝐦𝐨𝐝𝐞𝐥 𝐨𝐧 𝐚𝐧 𝐞𝐝𝐠𝐞 𝐝𝐞𝐯𝐢𝐜𝐞 using C++.⁣⁣ The device had limited power and memory, so every line of code had to be optimized. It wasn’t easy, but it was exciting and it introduced me to : 𝐄𝐝𝐠𝐞 𝐀𝐈.⁣⁣ ⁣⁣ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐄𝐝𝐠𝐞 𝐀𝐈?⁣⁣ It simply means running AI models directly on devices: cameras, sensors, gateways, or machines, instead of relying only on the cloud.⁣⁣ ⁣⁣ 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:⁣⁣ → Instant real-time decisions⁣ → Stronger privacy (data stays local)⁣ → Works even with poor connectivity⁣ → Less dependency on costly cloud processing⁣ ⁣⁣ At 𝐁𝐫𝐚𝐢𝐧𝐲 𝐍𝐞𝐮𝐫𝐚𝐥𝐬, we have carried this vision forward by building solutions across different edge devices:⁣⁣ ⁣⁣ » 𝐐𝐮𝐚𝐥𝐜𝐨𝐦𝐦 𝐐𝐂𝐒𝟔𝟏𝟎: Object detection in C++ for wildlife monitoring, reducing false alarms.⁣⁣ » 𝐈𝐧𝐭𝐞𝐥 𝐑𝐞𝐚𝐥𝐒𝐞𝐧𝐬𝐞 & 𝐎𝐮𝐬𝐭𝐞𝐫 𝐋𝐢𝐃𝐀𝐑: Smart surveillance that records only when real motion is detected.⁣⁣ » 𝐑𝐨𝐜𝐤𝐜𝐡𝐢𝐩 𝐑𝐊𝟑𝟓𝟖𝟖: Vehicle speed detection with real-time accuracy.⁣⁣ » 𝐑𝐚𝐬𝐩𝐛𝐞𝐫𝐫𝐲 𝐏𝐢: Automated bulk QR code scanning to speed up logistics.⁣⁣ » 𝐒𝐧𝐚𝐩𝐝𝐫𝐚𝐠𝐨𝐧 𝐍𝐏𝐄 & 𝐍𝐏𝐔𝐬: accelerated on-device AI workloads for faster inference and lower power use.⁣⁣ ⁣⁣ AI creates the most impact when it runs closest to the source; at the 𝐄𝐃𝐆𝐄.⁣⁣ ⁣⁣ #EdgeAI #ArtificialIntelligence #BrainyNeurals #EdgeComputing #Innovation #ComputerVision #AIonEdge #IoTDevices #Edge #AI

👋 Tech Trends Shaping 2025 — A Simple Overview I came across a great article from StartUs Insights highlighting 10 key technology trends shaping the future. Here’s a quick and easy summary: 1. Generative & Autonomous AI (Agentic AI) Smart systems that can “think,” make decisions, and learn on their own — without constant human input. 2. Quantum Computing Getting more powerful: able to solve problems that seem impossible today, especially in cryptography and materials science. 3. Cloud-Edge & 6G Powerful cloud infrastructure combined with edge devices = super-fast and reliable applications, like in smart cities. 4. Industrial Metaverse & Spatial Computing Enter digital twins of machines, train for repairs, or design in VR/AR. 5. Clean Tech Focus on sustainable production — from clean energy to eco-friendly materials. 6. Synthetic Biology Programming living systems: from synthetic tissues to biodegradable materials. 7. Smart Robots & Collaborative Automation Robots that work alongside humans — in healthcare, logistics, and manufacturing. 8. Cyber-Resilience & Data Security New approaches to protecting data, including post-quantum cryptography. 9. Space Manufacturing & Energy Building satellites or solar panels directly in orbit. 💫 Why it matters: Businesses become faster and more cost-efficient — testing ideas, scaling, and implementing ahead of competitors. The economy benefits: deep tech is expected to generate $700B in value by 2030 (48% annual growth). A chance to stay ahead in major areas: clean energy, autonomous systems, and quantum security. 💫 Prediction: Companies that adopt Agentic AI, Spatial Computing, and robotics first will lead the market. Others will need to catch up. Which of these trends excites you most? I’d love to hear your thoughts! #TechTrends #Innovation #AI #QuantumComputing #Robotics #DigitalTransformation #IndustrialMetaverse #SpatialComputing #CleanTech #SyntheticBiology #CyberSecurity #EdgeComputing #FutureOfWork #DeepTech #6G #EmergingTech

“Human Augmentation: How SAR Software Inc Can Build Transformative Solutions Blurring the Line Between Biology and Technology” ✅SAR Software Inc can leverage its expertise in AI, data engineering, custom software development, cloud solutions, and automation to design and build a robust human augmentation project that bridges the gap between biology and technology. This initiative could focus on developing intelligent platforms that enhance human capabilities using a mix of artificial intelligence, wearable devices, and biometric sensors. For instance, SAR Software Inc could engineer neuro-adaptive interfaces that allow users to control digital environments or physical machines directly with neural signals, using data science and machine learning algorithms to interpret those signals in real-time—a concept that directly supports brain-computer interface innovation as depicted in the image. ✅With extensive experience managing digital transformation, cloud-native architectures, and operationalizing generative AI, SAR Software Inc could create seamless solutions for healthcare, manufacturing, or enterprise productivity. In healthcare, a project might involve developing AI-powered exoskeletons or prosthetics with real-time sensor integration to boost rehabilitation outcomes or patient mobility. In industrial settings, smart wearables could use data analytics and IoT to monitor worker health and performance, increasing safety and reducing operational risks. ✅To deliver such systems, SAR Software Inc would utilize a tech stack that includes AI/ML frameworks (TensorFlow, PyTorch), cloud services (AWS, Azure, GCP), biometric sensors, and AR/VR platforms for immersive user experiences. Their team of specialists can ensure scalability, data security, and compliance—allowing organizations to adopt human augmentation technology confidently. By driving innovation in this sector, SAR Software Inc empowers businesses and individuals to transcend traditional limitations, paving the way for a future where artificial intelligence, machine learning, and human cognition converge in practical, life-changing applications. Learn more about our Success: https://lnkd.in/ewTpjG4t Learn more about our expertise: https://lnkd.in/dYPm37jv Learn more on this: https://lnkd.in/etaWA3dN #HumanAugmentation #SyntheticDatatoShapetheFutureofAI #MachineLearning#FutureOfWork #HealthcareInnovation#VirtualReality #DigitalTransformation#Blockchain#DataDrivenDecisionMaking#ArtificialIntelligence#AWS#DataAnalytics#CloudComputing#AIOps#BigData#DataEngineering

The Hidden Costs of Floating-Point Precision—and Why Your Business Should Care In an era where AI, financial modeling, and high-performance computing (HPC) demand unprecedented numerical accuracy, a fundamental yet often overlooked challenge persists: **floating-point arithmetic**. Despite being the backbone of modern computing, its nuances—rounding errors, precision loss, and edge-case behavior—can introduce silent but costly inefficiencies. A recent deep dive by Fabien Sanglard visually demystifies how floating-point numbers work under the hood, revealing why even seasoned engineers can misjudge their impact. For businesses, the implications are significant: - **Financial systems** risk cumulative errors in high-frequency trading or risk modeling, where micro-deviations compound over time. - **AI/ML pipelines** may suffer from unstable training or biased outputs if precision isn’t managed at scale. - **Embedded and IoT devices** face performance trade-offs when balancing speed and accuracy in real-time calculations. The stakes are higher than ever: A 2023 study by the IEEE found that floating-point-related bugs account for ~15% of critical failures in scientific and financial applications, yet many teams lack dedicated strategies to mitigate them. With the rise of quantum computing and neuromorphic chips, the need for precision-aware architecture will only grow. For CTOs and engineering leaders, the question isn’t whether floating-point precision matters—it’s how proactively you’re addressing it. Are your systems audited for numerical stability? Do your teams have the tools to detect subtle drift before it becomes a liability? At Tech & Data Insights, we track these foundational trends because the future of tech isn’t just about breakthroughs—it’s about mastering the details that separate robust solutions from fragile ones.How is your organization ensuring numerical integrity in an increasingly data-driven world? #TechLeadership #Computing #AI #FinTech #CTOInsights

𝗛𝗼𝘄 𝗟𝗟𝗠𝘀 & 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗔𝗿𝗲 𝗣𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗖𝘂𝘀𝘁𝗼𝗺 𝗔𝗜 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿𝘀—𝗕𝗲𝘆𝗼𝗻𝗱 𝗖𝗨𝗗𝗔 By 2025, visionaries aren’t just asking which #GPU wins—they’re asking which software stack enables breakthroughs. Welcome to the era where #LLMs unlock accelerators. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀  • #Huawei just open-sourced #CANN, its equivalent to #CUDA for Ascend AI GPUs. This move, steeped in geopolitics and the scramble for sovereignty, is aimed at breaking the two-decade dominance of CUDA and sparking ecosystem innovation.  • #Intel’s #OpenVINO 2025.2 now supports a rich set of LLMs (#Qwen3, #Phi‑4, #Mistral‑7B, #SD‑XL), runs smarter on built-in #NPUs with KV cache compression and #LoRA adapters, and optimizes inference across CPUs and GPUs. 𝙒𝙝𝙮 𝙙𝙤𝙚𝙨 𝙩𝙝𝙞𝙨 𝙚𝙭𝙘𝙞𝙩𝙚 𝙢𝙚 𝙖𝙨 𝙖 𝙥𝙧𝙤𝙙𝙪𝙘𝙩 𝙨𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙨𝙩? Because whether it’s for hyperscale or edge—driverless cars, wearables, or smart factories—the software layer is the superpower that unleashes hardware potential. 𝗧𝗵𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗳𝗼𝗿 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗟𝗲𝗮𝗱𝗲𝗿𝘀  1. Model → Runtime → Hardware (LLM → Software stack → NPU/TPU): Product success hinges on seamless alignment across all three.  2. Avoiding lock-in: CANN’s open move could enable a new generation of cross-platform AI design—if tools and documentation catch up.  3. Optimizing at the edge: OpenVINO’s NPU optimizations reshape the performance/usability calculus for on-device GenAI—essential for instantaneous inference and user trust. 𝗠𝘆 𝗧𝗮𝗸𝗲 Enterprise AI will continue to demand scale. But edge AI will only win with performance and portability—and that’s powered by software. The future belongs to those who design hardware and the frameworks that unlock it—especially when AI must run inside a car, a wearable, or a factory sensor under real-world constraints. If product teams can work across silicon, software, and strategy—what kind of hardware stacks will that unlock? Do you see a startup ecosystem growing around CANN? Or are frameworks like OpenVINO defining the standards that will shape edge AI’s future? The big question is: 👉 Will we see an open, cross-platform “lingua franca” for AI hardware emerge, or will ecosystems remain siloed under proprietary stacks? Comment below 👇 Let’s discuss. #AI #EdgeAI #EnterpriseAI #ProductLeadership #OnDeviceAI #MLInfrastructure #SoftwareEcosystems

In the rapidly evolving world of technology, Google’s Nano Banana AI stands out as a remarkable innovation that blends the precision of nanotechnology with the intelligence of cutting-edge AI. This advancement is not just about making AI smaller it’s about unlocking transformative possibilities. Nano Banana AI represents a shift in how we think about intelligence at scale. By shrinking powerful AI systems to nanoscale, Google is enabling real-time data processing and decision-making within devices so compact they can be embedded in everything from wearable health monitors to environmental sensors. This paves the way for hyper-personalized healthcare, smarter cities, and truly connected ecosystems. What excites me most is the convergence of sustainability, efficiency, and intelligence in this technology. Reducing power consumption while exponentially increasing the scope and reach of AI applications means we can build solutions that are both innovative and responsible. As we explore the future of AI, the potential for Nano Banana AI to redefine sectors such as medicine, environmental monitoring, and consumer electronics is immense. It challenges us to rethink the boundaries of what machines can do and how intimately they can integrate into our lives. Google’s Nano Banana AI is a glimpse into a future where intelligence is everywhere, accessible, and perfectly scaled to meet the demands of our complex world. #AI #Nanotechnology #Innovation #Sustainability #FutureOfTech #MachineLearning #HealthcareTech #SmartCities #Google #gemini

The generative AI revolution could widen the digital divide—or help us close it. Danilo Pietro Pau of STMicroelectronics challenges the status quo: Why should only a handful of companies control access to powerful AI? Instead, Pau paints a radically inclusive future—where GenAI runs directly on smartphones, thermostats, and microcontrollers. No data center required. What you'll learn: - Why hyper-centralized GenAI is unsustainable - How intelligent, natural interfaces can run at the edge - Real-world examples: style transfer, voice interaction, VQA - How to get started using audio, visual, and language models locally GenAI at the edge isn't just technically possible—it’s necessary if we want a more equitable, sustainable AI future. Full talk → https://lnkd.in/gqzAbP46 #EdgeAI #GenerativeAI #DigitalInclusion #TinyML #AIInterfaces #STMicroelectronics #TechForGood #MachineLearning #ArtificialIntelligence #DigitalEquity #EdgeComputing #FutureOfAI Pete Bernard Ed Doran Ph.D. Hajar Mousannif

This Week’s Tech News Points to AI’s Growing Role in Innovation & Responsibility 🤖 🔍 What’s New U.S.-UK Tech Alignment: A major multibillion-dollar agreement is in the works between the U.S. and the UK to increase cooperation in AI, semiconductors, quantum computing, and telecoms. (Reuters) AI Attack Risks Rising: Experts are warning of a new kind of “zero-day” threat — attacks powered by autonomous AI agents that can adapt and exploit personalized vulnerabilities. (Axios) Consumer Tech Continues to Push Boundaries: From Google’s next-gen Nest Cams to Sony’s recent Xperia phone design and Qualcomm’s “Quick Charge 5+” feature — innovation in hardware is still accelerating. (WIRED) 💡 Key Takeaways 1. AI is now firmly both an innovation engine and a risk vector. What used to be speculative — autonomous agents, threat models that adapt — are no longer “what ifs.” They’re making headlines. This makes investment in AI safety, governance, and detection/response tools non-optional. 2. Policy & strategy are catching up. Governments aren’t just reacting; they’re pre-emptively trying to build structures (regulatory, partnerships) so AI’s impact is managed responsibly. The US-UK agreement is a strong example. 3. Hardware & UX still matter. While AI gets most of the attention, improvements in user experience, charging tech, image/video quality, and integrated devices show that foundational engineering continues to be crucial. Everything from the smart home to mobile phones is getting smarter & more efficient. 🔭 Why I'll be watching 1. How companies in regulated industries will adopt stricter AI-governance frameworks. 2. What new tools will emerge for detecting/responding to AI-driven threats (detection & defense). 3. How hardware innovations will complement AI — especially edge devices, wearables, IoT — enabling more on-device computation, lower latency, better privacy.

🚀 What happens when AI data centres run out of space? NVIDIA thinks it has the answer. As AI models grow in size and complexity, the demand for computational power is pushing traditional data centres to their limits. Building ever-larger facilities is costly and often unsustainable. 💡 Enter NVIDIA Spectrum-XGS Ethernet — a new networking technology designed to connect AI data centres across vast distances, effectively transforming them into “giga-scale AI super-factories.” 🔑 Key innovations include: • Distance-adaptive algorithms for efficient long-range networking • Advanced congestion control to prevent bottlenecks • Precision latency management for predictable performance • End-to-end telemetry for real-time optimisation 🌍 CoreWeave will be among the first to deploy this technology, unifying multiple data centres into a single AI supercomputer. Why this matters: Instead of overloading local power grids or building massive single-site facilities, companies could distribute infrastructure across locations while still achieving near-seamless performance. Jensen Huang calls this the next stage of the AI industrial revolution — but the real test will be in how well it performs under real-world conditions. ⚡ If successful, this could reshape how the AI industry scales infrastructure and delivers services. 👉 What do you think: Will “scale-across” become the new standard for AI data centres? #AI #DataCenters #NVIDIA #Networking #DigitalTransformation #CloudComputing

Artificial Intelligence has moved beyond hype to become the driving force of innovation. But behind every AI breakthrough is an invisible backbone—data centers, semiconductors, networks, and governance systems—that make it possible. Investing in AI infrastructure today isn’t just about keeping up with technology; it’s about shaping the intelligent, sustainable, and competitive digital economy of tomorrow. Know How: https://lnkd.in/gG2Gpbz7 #AI #Infrastructure #FutureOfTech #DigitalTransformation #ArtificialIntelligence #Sustainability