How AI will automate 70% of work tasks by 2026

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

Welcome to the Era of 1-bit LLMs! The future of AI is becoming leaner, faster, and more accessible! As large language models (LLMs) continue to power breakthroughs in automation, customer support, healthcare, and more, one of the biggest challenges remains — computational cost and energy consumption. That’s where 1-bit precision fine-tuning comes in. By reducing model precision to its bare minimum while maintaining performance through techniques like error compensation and gradient scaling, we are opening doors to: Ultra-efficient AI that runs on edge devices Faster training with less memory Sustainable, low-power solutions for real-world applications From smarter customer support bots to AI assistants on smartphones and IoT devices — the era of 1-bit LLMs is set to redefine how we deploy and scale AI across industries. Let’s embrace this new frontier and innovate responsibly! reference - https://lnkd.in/dUMRiCen #AI #LLM #1bitAI #Quantization #EdgeAI #SustainableTech #MachineLearning #DeepLearning #CustomerSupport #Innovation #FutureOfAI

🚀 Exploring AI at the Edge: Today & Tomorrow AI is making strides by moving closer to where data originates — on devices, sensors, and machines — rather than relying solely on cloud processing. This transition brings forth accelerated speed, heightened privacy, and instantaneous intelligence. 🔑 Key Opportunities: - ⚡ Achieving ultra-low latency for immediate decisions (e.g., autonomous cars, robotics) - 🔒 Ensuring enhanced privacy & security by keeping data on the device - 🌍 Scaling capabilities across billions of IoT devices - 📶 Functioning seamlessly even with limited or no connectivity ⚠️ Challenges Ahead: - 🧩 Addressing hardware limitations (compute power, memory, energy consumption) - 🔄 Optimizing models through techniques like tiny ML, quantization, and pruning - 🛡️ Managing governance & updates (such as patching AI models at the edge) - 🔮 Future-proofing strategies encompassing standards, interoperability, and ethical considerations 👉 Present Scenario: Edge AI is currently fueling advancements in smart cameras, wearables, and predictive maintenance. 👉 What's Next: Anticipate a future with hyper-personalized healthcare, intelligent cities, and cutting-edge innovations in various sectors.

🚀 Exploring the Future of AI: Embracing Invisible Technology 📍 Samsung recently unveiled the concept of "Invisible AI," where intelligence operates in the background without constant user interaction. 👉 Envision: 🚀 Your fridge recommending recipes based on its contents. 🚀 Your washing machine adapting cycles according to fabric types. 🚀 Your phone optimizing battery usage based on your habits. 🌐 This transcends traditional AI chatbots, introducing ambient AI seamlessly woven into daily routines to enhance efficiency and conservation of resources. 💡 Significance for the IT & business community: 📍 Shifting focus from "features" to "experiences" in design thinking. 📍 Increasing demand for expertise in embedded AI, IoT, and data security. 📍 Preparation for a future where technology effortlessly integrates into daily life, simplifying complexity for users. 🌐 The pivotal question remains — will individuals embrace AI that operates subtly in the background? 🔗 Share your thoughts: Are you enthusiastic about AI seamlessly blending into everyday life, or does the concept of "invisible AI" raise concerns regarding privacy and control? #ArtificialIntelligence #InvisibleAI #AmbientAI #FutureOfTech #Innovation #IoT #SmartDevices #DigitalTransformation #TechTrends #AIInEverydayLife

Is the Transformer architecture dead? 🤯 Google DeepMind just unveiled a new AI model that's 2x faster and uses half the memory. Imagine the traditional Transformer as a hospital where every patient (or "token") goes through every single department, regardless of the ailment. MoR, or Mixture-of-Recursions, is a new kind of hospital. Its lightweight "router" intelligently triages each token, sending simple ones home quickly while routing complex ones for deeper, recursive passes. Here’s why it's a paradigm shift: Smarter, Not Bigger: Reuses a single set of shared layers, dramatically cutting down on parameters. Inference Efficiency: The result is up to 2x faster inference and a 50% reduction in memory usage. Democratizing AI: This efficiency could bring more powerful AI to resource-constrained devices, from mobile phones to IoT. This isn't just an optimization; it's a fundamental rethinking of how LLMs reason and use computational resources. What are your thoughts? Is this the beginning of the post-Transformer era, or just an exciting new path forward? Share your take below! 👇 #AI #MachineLearning #DeepLearning #LLM #MoR #Transformer #GoogleDeepMind #TechInnovation

I love technology in all its forms. AI is reshaping IoT, and IoT is also reshaping AI. What do I mean? AI makes IoT more intelligent, extracting patterns, predicting failures, enabling decisions that raw data alone could not provide. At the same time, IoT makes AI more grounded, feeding it with reliable data from the physical world, without good sensors, AI is blind. But lately I see a growing obsession: applying AI to solve every single problem, even the simplest ones. Cameras with computer vision everywhere. Yet, airplanes have been flying on autopilot for decades thanks to sensors, not artificial intelligence. Why should “innovation” always mean forcing the use of the most complex technology, when simpler solutions exist? Why use a camera to measure height, when, for example, an ultrasonic sensor can do it more robustly? Why analyze thousands of images to detect a door opening, when a magnetic reed switch gives the same information instantly? Why use AI to detect water in a tank, when a float sensor costs a fraction and never needs training? Sometimes, the true innovation lies in understanding how simple sensors can empower AI at a higher, logical level. And sometimes, it lies in not using AI at all. Think about how elegant the principle behind a fuel pump nozzle is: it stops automatically when the tank is full, without electronics, sensors or software. Just physics. Simple, robust, brilliant. A good engineer’s job is to simplify, not complicate. That’s what Occam’s razor teaches us. So before firing the AI cannon at a mosquito-sized problem, let’s carefully, really carefully, explore all possible solutions.

AI Development Trends Shaping 2025 🤖✨ Swipe 👉 to discover how Artificial Intelligence is changing the world around us: ⿡ Generative AI is creating text, images & even code in seconds! ⿢ AI Automation is transforming healthcare, finance & education. ⿣ Edge AI is powering self-driving cars & smart IoT devices. ⿤ Responsible AI ensures ethics, fairness & transparency. 🔖 Save this post to stay ahead in 2025! #AIDevelopment #FutureOfAI #TechTrends2025 #ArtificialIntelligence #AITech #Innovation

🚀 What Are Digital Twins? The Future of Smart Innovation --- 👀 What if you could test-drive the future before it happens? That’s exactly what Digital Twins let companies do. From hospitals to aerospace, this technology is quietly reshaping entire industries. --- 👀 Which the next decade, companies without Digital Twins will fall behind. Why? Because their competitors will already be simulating, predicting, and fixing problems before they even happen. --- The bridge between physical and digital worlds 🌍💻 That’s exactly what Digital Twins make possible. --- 🔍 So, What Exactly Is a Digital Twin? At its core, a digital twin is just a virtual copy of a physical object, process, or system. But here’s the cool part: it’s not static. It’s “alive” because it uses real-time data, IoT, sensors, and AI to mirror what’s happening in the real world. In plain words → it’s like having a living, breathing model of your operations. --- 🏭 Real-World Examples (That Are Happening Right Now) This isn’t theory. Companies are already using digital twins in ways that sound futuristic: Manufacturers predict machine breakdowns before they happen ⚙️ Hospitals test treatments on digital models of patients 🧬 Smart cities run simulations to reduce traffic jams and energy waste 🏙️ Aerospace companies use them to check aircraft parts and improve flight safety ✈️ --- 🌟 Why It Matters The market for Digital Twins is exploding (valued in the billions and growing fast). Why? Because they save time, cut costs, and help businesses stay one step ahead. I like to think of it this way: 👉 If data is the new oil, then Digital Twins are the refineries turning that data into real value. --- ✨ Final Thought Digital Twins aren’t just about tech. They’re about changing the way we see, predict, and create the future. > “The best way to predict the future is to simulate it.” --- 📌 Hashtags #DigitalTwins #Innovation #FutureOfWork #IoT #AI #SmartCities #Manufacturing #TechnologyTrends #DigitalTransformation

🚀 SLMs and the Future of Edge Tech As AI adoption accelerates, one of the most exciting shifts we’re witnessing is the rise of Small Language Models (SLMs), optimized, efficient LLMs designed to run on edge devices instead of the cloud. Why does this matter? 🌐 Privacy & Security: Sensitive data can be processed locally without sending it to centralized servers. ⚡ Low Latency: Real-time responses without depending on internet bandwidth or server round-trips. 🔋 Efficiency: Tailored architectures make them energy-efficient, enabling deployment on mobile, IoT, and embedded systems. 🛠️ Customization: SLMs can be fine-tuned for domain-specific use cases (industrial IoT, automotive, healthcare devices, etc.) at a fraction of the cost. 🔮 What’s next? We’ll see SLM-powered smart assistants embedded directly into devices — from wearables to autonomous machines. Federated learning + SLMs will allow collaborative intelligence across devices without compromising user data. Integration with 5G/6G edge infrastructure will amplify real-time AI at scale. Enterprises will shift toward hybrid AI stacks — large foundation models in the cloud and specialized SLMs at the edge. The future of AI won’t just be about bigger models. It will be about smarter, smaller, and closer-to-the-user models — making intelligence ambient, accessible, and responsible. 💡 Would love to hear your thoughts: Where do you see SLMs creating the biggest disruption — in consumer devices, industrial systems, or enterprise workflows? #AI #EdgeComputing #SLM #FutureTech #ArtificialIntelligence

$AUKI is unlocking AI's next chapter - copilots built to work with humans physically. Smart glasses and wearables that guide tasks, suggest next moves, and help you collaborate. Think this is the end of human work? NO! It's the start of working better, faster, smarter. 🔗Article: https://lnkd.in/gUHJG6mz