<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/9fea234dcc8c48fea29c51d493c0ecb9&quot; frameborder=&quot;0&quot; width=&quot;1738&quot; height=&quot;1303&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1303</height><width>1738</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1303</thumbnail_height><thumbnail_width>1738</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/9fea234dcc8c48fea29c51d493c0ecb9-3f0375d726f6e4b1.gif</thumbnail_url><duration>261.595</duration><title>AI-Powered Data Anomaly Detection</title><description>In this video, I share my recent exploration of using AI to identify patterns in data, specifically through a prototype I built over the last four days. I demonstrate a tool that analyzes office visit records and flags any anomalies, showcasing a dashboard with key metrics like 1,500 total visits. I also discuss the AI functionality powered by OpenAI and how it interacts with the data. I would love to hear your feedback or any ideas you have related to these concepts.</description></oembed>