Developing a BCI-inspired crash detection system with ML

The Rise of Small Language Models (SLMs) in Agentic AI Imagine these scenarios: Your smartwatch detects an irregular heartbeat and alerts you instantly — no cloud roundtrip, no delay. A warehouse robot avoids a falling box in real-time by running its decision model on-device. A student’s tablet adapts a math quiz on the spot without internet dependency. These are cases where Large Language Models (LLMs) fall short — they’re too heavy, too slow, and too costly for real-time, edge-level intelligence. NVIDIA Research’s recent paper underscores this: SLMs = efficiency → low compute, low cost, low latency. SLMs = modularity → specialized tasks handled locally, with LLMs only when broader reasoning is required. SLMs = scalability → intelligence embedded in devices we use daily, from wearables to industrial IoT. The takeaway: the future of agentic AI isn’t one massive brain, but ecosystems of SLMs + LLMs working together — combining speed, specialization, and general reasoning. 👉 Where do you see SLMs unlocking possibilities in your industry that LLMs simply can’t? #AI #SLM #LLM #AgenticAI #EdgeAI #Future #AGI

One of the most exciting frontiers in AI right now isn’t bigger models — it’s smarter hardware. Neuromorphic computing mimics the human brain using spiking neural networks that only activate when needed, consuming up to 80% less energy than today’s systems. Recent breakthrough: Researchers at the Indian Institute of Science developed a molecular memristor that can store and process information in 16,500 states (vs. traditional binary 0/1). That means AI tasks like training large models could one day run on personal devices, not just giant data centers. 💡 Why this matters: Sustainable AI → reducing massive training costs (GPT-4 training ≈ 300,000 kWh of power ⚡) Real-time adaptability for robotics, IoT, and autonomous systems Brings AI closer to human-like learning efficiency At Kloudidev, we believe neuromorphic tech represents a turning point — making AI not just powerful, but also sustainable and scalable everywhere. #NeuromorphicComputing #AIRevolution #SustainableAI #Innovation

We’ve seen this movie before 🍿: the internet left our desks and moved into our pockets -- and everything changed. The next shift is here: AI agents are moving from the cloud to the edge ☁️➡️📲. Why now? Small Language Models (SLMs) plus techniques like quantization, pruning, and distillation make on-device inference real. The result feels less like waiting on a server and more like talking to your own JARVIS 🤖-- only private, compliant, and fast. What this unlocks: ⚡ Responsiveness that feels conversational, not like a loading bar 🔒 Trust by design: your data stays on your device or within your walls 🔋💸 Lower ongoing cost/energy and fewer trips to the cloud 🎯 Deep personalization across IoT, robotics, and autonomous systems Imagine the possibilities: 🏠 Smart homes that anticipate your needs without pinging a server. 🏭 Factory robots coordinating tasks on $99 devices. 🩺📱 Personalized health advisors running entirely on your phone. Just as the mobile revolution unlocked billions of new interactions, edge AI will spawn new products, business models, and careers. Which AI-powered experience do you wish lived on your device instead of in the cloud? 💡🤔 #AI #EdgeAI #OnDeviceAI #AgenticAI #SLM #LLM #GenAI #TinyML #LLMOps #IoT #SmartDevices #Robotics #OfflineFirst #PrivacyByDesign #DataSecurity #FederatedLearning #EmbeddedAI #MobileAI #RAG #Quantization #Distillation #Pruning #ComputeAtTheEdge #Mistral #Llama #Phi3 #FutureTech #EdgeComputing #OnPrem

Google's "𝐍𝐚𝐧𝐨 𝐁𝐚𝐧𝐚𝐧𝐚 𝐀𝐈" is making waves with its innovative approach to machine learning. While the name might sound whimsical, the technology behind it is anything but the underlying technology demonstrates serious advancements in context-aware and high-precision image manipulation. Nano Banana AI is designed for hyper-efficient, small-scale AI models, perfect for on-device processing and applications where resources are limited This innovation has massive implications for industries ranging from IoT and smart manufacturing to healthcare and personalized consumer tech. It's all about bringing powerful AI closer to the data, reducing latency, and opening up possibilities we're just beginning to explore. 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐲𝐨𝐮𝐫 𝐭𝐡𝐨𝐮𝐠𝐡𝐭𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐨𝐟 𝐡𝐲𝐩𝐞𝐫-𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐀𝐈 𝐥𝐢𝐤𝐞 𝐍𝐚𝐧𝐨 𝐁𝐚𝐧𝐚𝐧𝐚? 𝐒𝐡𝐚𝐫𝐞 𝐲𝐨𝐮𝐫 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐛𝐞𝐥𝐨𝐰! #AI #MachineLearning #GoogleAI #NanoBananaAI #Innovation #EdgeAI #Tech

🚀 Exciting Learning Experience! 🚀 Recently, I had the opportunity to attend an AI Tools Workshop Be10x, where I explored how cutting-edge technologies are shaping the future of engineering and problem-solving. Throughout the session, I gained hands-on exposure to practical AI applications, from automation and analytics to productivity-enhancing tools that can streamline workflows. It was inspiring to see how AI can empower engineers like me to design smarter, more efficient solutions. 💡 Key Takeaways: Leveraging AI for faster prototyping and product design. Exploring AI-driven insights in system performance optimization. Understanding how AI tools can support documentation and lifecycle management. Grateful for this opportunity to learn, interact, and expand my technical toolkit. Excited to apply these insights in future projects. 🌐✨ #AI #ArtificialIntelligence #Learning #Engineering #IoT #Innovation #be10x

🚀 Cracking AI & Data Science Interviews Topic: Edge AI – Bringing Intelligence to Devices Quick Insight: Edge AI enables machine learning models to run directly on devices like smartphones, IoT sensors, and industrial machines, reducing latency, bandwidth use, and dependency on cloud infrastructure. --- 1️⃣ Core Concepts: On-Device Inference: Perform predictions locally without sending data to the cloud. Model Compression: Techniques like pruning, quantization, and knowledge distillation to make models lightweight. Energy Efficiency: Optimizing computation for low-power devices. Real-Time Processing: Immediate responses for time-critical applications. --- 2️⃣ Key Techniques & Tools: TensorFlow Lite / TensorRT / ONNX Runtime for deploying optimized models. Pruning & Quantization: Reduce model size while maintaining accuracy. Federated Learning: Train models across devices without centralizing data. Edge Accelerators: GPUs, TPUs, and NPUs designed for edge computation. --- 3️⃣ Use Cases: ✅ Smartphones: Voice assistants, camera enhancements. ✅ IoT & Industrial Automation: Predictive maintenance, anomaly detection. ✅ Autonomous Vehicles: Real-time object detection and navigation. ✅ Healthcare Devices: Portable diagnostic tools, patient monitoring. ✅ Retail: On-device customer behavior analysis and personalized recommendations. --- 4️⃣ Interview Questions to Expect: “What are the challenges of deploying AI on edge devices?” “Explain model compression techniques and their trade-offs.” “How does federated learning work?” “Why is Edge AI important for real-time applications?” “Give examples of Edge AI in industry or consumer devices.” --- 🔥 Trending Insight: With the rise of IoT and 5G, Edge AI is becoming crucial for privacy-preserving, low-latency, and scalable AI applications across industries. #EdgeAI #FederatedLearning #IoT #MachineLearning #DeepLearning #AI #InterviewPrep

Another weekend, another chance to jump into #IoT #vibecoding! This time I experimented generating tones and frequencies on an #ESP32 board with built-in Bluetooth. In the end I programmed a Bluetooth speaker to play the riff from The Knack’s “My Sharona” using generated tones and pauses. Who says learning can’t be fun? Here’s what frustrated me. 👇 I uploaded the sheet music and asked AI to tell me the notes, their frequencies, and the length of each note and rest in milliseconds based on the studio version’s original tempo. It’s all mechanical: 🎶 Read the notes 👀 Look up reference values 🟰 Do a few calculations. Shouldn’t AI nail this? Turns out … nope! 👎 ChatGPT was useless. 👎 Claude was useless 👎 Gemini was useless. Those guys can’t read music … yet. It was faster for me to read the notes and look up their frequencies (there are only four of them!) and then calculate that 147–148 bpm translates to 204 milliseconds per eighth note. (I love that there’s slight tempo variation in the recording, an artifact of a time when #RockAndRoll drummers worked without a click track.) So yeah—I did it manually. I know there are AI tools that specialize in music. Has anyone used them? And to any #AI power users: how would you have approached this?

🤖 Machine Learning is at a turning point The next wave of research is reshaping how we build and use intelligent systems: • Explainable AI (XAI) – making black-box models more transparent and trustworthy especially in fields like healthcare and finance • TinyML – bringing ML to ultra-low-power devices unlocking smart features in wearables, sensors and IoT • Multi-Modal AI – teaching machines to understand and connect text, images and speech at once (think ChatGPT with vision + voice) These areas aren’t just buzzwords they’re solving real challenges like trust, accessibility and richer human-AI interaction. 👉 Which of these do you think will impact our daily lives first? 💭Comment below! #AIClub #AIClubSNIST #Trends #MachineLearning #AI #ArtificialIntelligence #Tech #FutureOfAI #MLResearch #DataScience #AIC #TechNews

👏 Big appreciation to Vivek Kumar for this innovative experiment with Google Text-to-Speech (gTTS) and Axis Network Speakers! 🎙️ Pipeline in action: 1️⃣ Generate dynamic speech with gTTS 2️⃣ Convert audio → 8kHz μ-law 3️⃣ Stream live via Axis transmit.cgi API ✅ Success → Audio plays directly on the device ⚠️ Challenge → Playback is a bit “chunky,” likely due to packet pacing & timing drift 🔧 Next steps: ⏱ Fine-tune packet scheduling (20ms frames) 🎶 Optimize buffering for smoother playback 🤖 Integrate AI-driven alerts & multilingual voices 🚀 It’s still a work in progress, but seeing real-time TTS streaming on Axis speakers is a huge step forward for AI + IoT audio applications. 👉 What do you think — where could real-time TTS on network speakers make the biggest impact? hashtag#Innovation hashtag#AxisCommunications hashtag#IoT hashtag#AudioStreaming hashtag#TextToSpeech hashtag#Python hashtag#AI hashtag#EdgeAI hashtag#gTTS hashtag#VAPIX hashtag#AudioTech