How AI, ML, LLM, and CV work in IoT - ObjectSpectrum blog

The #SKILIKET project is taking #CitizenScience further by integrating #AI for predictive analytics, pattern recognition, and reflective learning 🌍🤖📊. Our new publication explores how merging #AI with #IoT data (#AIoT) creates new pathways for civic participation and #socioecological understanding: 👁️ Article ➡️ : https://lnkd.in/e8EaquAJ Grateful to coauthor with Jose Guadalupe Mercado Rojas, Dr. Rasikh Tariq, Juan Alvaro Marchina Herrera and Inna Artemova #EnvironmentalEducation Tecnológico de Monterrey

I am pleased to share our new article published in IEEE Revista Iberoamericana de Tecnologías del Aprendizaje (IEEE-RITA) titled “Framework for AI Integration in Citizen Science: Insights From the SKILIKET Project.” This work highlights how AI can be meaningfully integrated into citizen science to strengthen environmental education by combining sensor data with human observations, enabling participants to recognize patterns, reflect critically, and connect scientific models with real-world experiences. The study outlines a pedagogical framework that promotes critical thinking, civic engagement, and proactive environmental stewardship through technology-enhanced learning. Many congratulations to all the co-authors and specially the leadership of Professor Jorge Sanabria-Z .

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

ActiveTech Systems hit $120M revenue with 450 employees. Their secret? AI models that run on your phone, not in the cloud. The pivot changed everything. The future of AI isn't just about bigger models. It's about smarter, more efficient ones. As a CS student specializing in AI-ML, I'm fascinated by two major trends reshaping our field: 🔄 Multimodal AI: •Combines text, images, audio, and video •Enables more natural human-AI interaction •Powers next-gen virtual assistants and medical diagnostics 💡 Edge AI & Small Models: •Runs directly on your phone or IoT devices •Reduces cloud dependency and costs •Better privacy and faster response times Here's what's impressive: Over 60% of new enterprise AI now includes on-device processing. Why? Because running AI locally isn't just faster - it's smarter. The numbers tell the story. Edge AI adoption is up 40% year-over-year. Companies like ActiveTech are proving this model works, hitting $120M revenue by focusing on efficient, lightweight AI solutions. Think about it. Would you rather wait for your data to travel to a distant server, or have AI right on your device? What's your take on edge AI? Have you noticed any AI-powered apps running faster on your phone lately? #EdgeAI #ArtificialIntelligence #TechTrends

📡 How AI Collects and Prepares Data Every smart AI system starts with high-quality data. Collecting and preparing that data is the foundation for accurate, trustworthy results. 🔹 1️⃣ Data Collection AI gathers information from: Public sources – websites, open datasets, research papers User interactions – clicks, searches, voice commands (with consent) Sensors & IoT – cameras, smart devices, environmental monitors Enterprise databases – sales records, support tickets, operational logs 🔹 2️⃣ Data Cleaning Raw data often contains errors or duplicates. AI teams remove noise, fix mistakes, and filter out irrelevant information. 🔹 3️⃣ Data Labeling Important for machine learning, labeling means tagging images, text, or numbers so the AI understands what each item represents (e.g., “cat,” “urgent,” “fraudulent”). 🔹 4️⃣ Organizing & Securing Prepared data is stored in structured formats and protected with strong privacy and security standards before training begins. ✨ The takeaway: clean, well-labeled, secure data = smarter and more reliable AI models. 🚀 #AI #DataCollection #MachineLearning #ArtificialIntelligence #DataPreparation #FutureOfWork #DataScience

🚀 AI Industry Update: What's Shaping the Future? 🚀 The pace of AI innovation continues to accelerate, with several key trends dominating the landscape: 🔹 Generative AI is evolving beyond text and images to video, code, and even 3D modeling, revolutionizing creative workflows. 🔹 Edge AI is gaining traction, enabling real-time decision-making in IoT devices, autonomous systems, and healthcare applications. 🔹 Ethical AI and governance frameworks are becoming critical as regulations tighten globally, emphasizing transparency and fairness. Growth areas include AI-driven cybersecurity, personalized healthcare diagnostics, and sustainable solutions like optimizing energy consumption. Opportunities? Now is the time to upskill in AI/ML, explore cross-industry applications, and invest in responsible AI development. What trends are you most excited about? Share your thoughts below! 👇 #ArtificialIntelligence #AI #MachineLearning #TechTrends #Innovation

🎓 Just completed the AI in Connected Products (AIOT) course! 🚀 Gained skills in Artificial Intelligence, NLP, and IoT — excited to apply them in real-world projects. 💡 #AI #IoT #AIoT #ContinuousLearning #FutureTech

🚀 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

🤖 AI Development Trends: The Transition from Tool to Ecosystem Artificial intelligence has moved from the lab to the masses, from a "trial" for businesses to a "must." In 2025, several key trends in AI development are accelerating: 1️⃣ Multimodal AI: The integrated application of text, voice, image, and video will enable AI to move beyond just "talking" to "seeing," "hearing," and "understanding." 2️⃣ Industry-Specific Models: Smaller, more accurate models are emerging in fields such as healthcare, finance, and manufacturing, helping businesses implement applications more quickly. 3️⃣ AI + Automation: From code generation to process optimization, AI is gradually becoming the "second engine" of business operations. 4️⃣ Edge AI: AI no longer relies on the cloud; more computing will be performed locally on devices, empowering IoT, wearables, and smart devices. 5️⃣ Responsible AI: With stricter regulations, transparent, explainable, and fair AI systems will become a must-have for businesses. 🌍 AI is not just a technological trend; it's also a core force for organizational transformation and the reshaping of competitiveness. Whether a company can seize this wave of trends will determine its competitive position over the next 5–10 years. 👉 What changes has AI already brought to your industry? #AI #Artificial Intelligence #FutureTrends #EnterpriseStrategy #DigitalTransformation

Computer vision is a foundational element of a transformative Physical AI system. By far, no other modality can match the richness and depth of information provided by video footage. #ComputerVision #OperationalEfficiency This capability enables a shift from rigid automation to adaptive intelligence, with applications that are already driving measurable outcomes: - In manufacturing, vision systems detect defects instantly, improving quality control. - In automotive service, vision-driven solutions track service bay utilization to surface bottlenecks and increase throughput. - In asset tracking, computer vision and IoT transform legacy yards into intelligent ecosystems, eliminating manual searches. These applications show how Physical AI, powered by vision, can unlock new efficiencies and competitive advantages. https://lnkd.in/gHdm5pBF