Browsing: Detection

Enterprises are deploying autonomous agents to carry out complex, multi-step tasks across applications, databases and operational systems. This ability to act across systems can automate substantial work, but it also creates opportunities for unsafe or unauthorized behavior. 

The next phase of artificial intelligence (AI) at the edge for defense applications will not be defined simply by whether a model can run onboard. It will be defined by whether detection, classification, and tracking can work together quickly and reliably enough to preserve understanding as the mission unfolds. For first-person-view (FPV) and counter-uncrewed aerial…

This project demonstrates a real-timemotor vibration anomaly detection systemusing an STM32 microcontroller, GY-61 3-axis accelerometer, Arduino IDE, and NanoEdge AI. The system captures vibration signals from a rotating motor, collects normal and abnormal operating data, and uses NanoEdge AI Studio to benchmark and select a suitable Machine Learning library.

A configurable, instruction-driven detector that runs on any large language model (LLM) managed on Amazon Bedrock, evaluated on five public PII corpora across nine LLM-based detectors, including the OpenAI PrivacyFilter.