AI content generation is about feeding your AI models with semantic and contextual information. The result is a platform that can ‘understand’ what an item is, and how it should be used. AI creates content using semantic knowledge in any form of content including video, 3d, VR and more.
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Latest Artificial Intelligence Content News
Robust Serialization with JSON and Custom Types
In this article, you will learn how to build a robust JSON serialization pipeline in Python that handles custom data types, graceful error recovery, and…
GHARP: Real-Time Gaussian Head Animation from Large-Scale Reconstruction Prior
We present GHARP (Real-time Gaussian Head Animation from Large-scale Reconstruction Prior), a method that animates 3D human heads in real time from a few input images of a subject and a driving expression signal. We decouple the problem into an identity stage that builds a representation of the subject’s geometry…
Making Failure Diagnosis Reliable with Pensive AI
By Praneeth Yenugutala, Victor Dusa, Anastasia Samoilova, Megan Ren, Justin Wei, Marcelo Mayworm At Netflix, data and machine learning practitioners run millions of workloads each day across orchestration systems and compute engines such as Titus, Spark, Trino, and Druid. On peak days, roughly 100,000 workloads fail. When workloads fail, the failure…
Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3
Fleet-wide computer vision model lifecycle management is a problem that shows up the moment a quality inspection proof of concept succeeds. One camera and one model work fine. Then the plant asks for the same result on 40 lines, each with several cameras, and every camera sees the world differently.…