What is Edge AI and why does it matter that AI can run on a chip the size of a fingernail?

The answer

Edge AI refers to running artificial intelligence algorithms directly on local hardware devices - smartphones, sensors, microcontrollers, cameras - rather than in the cloud. TinyML, a subset of edge AI, deploys machine learning models on microcontrollers with as little as 256 KB of memory, consuming milliwatts of power. Google's TensorFlow Lite Micro and tools like Edge Impulse have made it possible to run image classification, keyword spotting, and anomaly detection on devices costing under $1. Applications include wildlife monitoring sensors that classify animal sounds in remote forests, predictive maintenance in factories, and real-time health monitoring in wearables. The edge AI market is projected to exceed $100 billion by 2028, driven by the need for low-latency, privacy-preserving, and always-available AI.