Editing Edge AI and the Architecture of the Periphery (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Edge AI''' — The deployment of artificial intelligence applications directly on physical devices at the "edge" of the network (smartphones, IoT devices, cars), allowing computations to be done locally rather than relying on centralized cloud servers.
* '''Cloud AI (The Alternative)''' — The traditional model where devices collect raw data, send it over the internet to a massive centralized data center for AI processing, and wait for the result.
* '''Latency''' — The time delay between a command and the response. Cloud AI suffers from high latency (network lag). Edge AI possesses near-zero latency because the math happens physically inside the device.
* '''Quantization''' — The mathematical magic that makes Edge AI possible. Standard AI models use massive, highly precise 32-bit decimal numbers. Quantization aggressively compresses the neural network by rounding those numbers down to 8-bit or 4-bit integers. It drastically shrinks the size of the AI file (e.g., from 16GB to 4GB) so it can fit on a smartphone chip, with almost zero loss in intelligence.
* '''Pruning''' — Another compression technique. The algorithm identifies and literally deletes the "synapses" (parameters) in the neural network that are rarely used, physically shrinking the model while retaining its core capabilities.
* '''NPU (Neural Processing Unit)''' — Specialized, highly efficient microchips built directly into modern smartphones and laptops, designed explicitly to do the specific tensor-math required by neural networks vastly faster and with less battery drain than standard CPUs.
* '''Federated Learning''' — A brilliant, privacy-preserving Edge AI training method. Instead of sending your private text messages to Google to train their AI, the AI trains itself *locally* on your phone. It then sends just the mathematical "updates" (not your texts) to the cloud to combine with millions of other phones, creating a smarter global model without ever exposing private data.
* '''Bandwidth Conservation''' — A massive economic driver for Edge AI. A city with 10,000 security cameras cannot physically stream 10,000 HD video feeds to the cloud 24/7; the internet would crash. Edge AI puts the neural network inside the camera. The camera processes the video locally and only sends a 1-kilobyte text alert if it detects a crime.
* '''TinyML''' — The extreme frontier of Edge AI. Shrinking machine learning models so incredibly small (a few kilobytes) that they can run on microcontrollers powered by a single coin-cell battery for years (e.g., agricultural sensors in a field).
* '''The Air-Gapped Environment''' — Devices physically completely disconnected from the internet for security or geographic reasons (submarines, deep-space probes, highly secure military bunkers). Edge AI is the only way these devices can possess intelligence.
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