Learning Long-Term Motion Embeddings for Efficient Kinematics Generation

Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scene dynamics, exploring multiple possible futures through full video synthesis remains prohibitively inefficient. We model scene dynamics orders of magnitude more efficiently by directly operating on a long-term motion embedding that is learned from large-scale trajectories …

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Scaling Camera File Processing at Netflix

Orchestrating Media Workflows Through Strategic Collaboration Authors: Eric Reinecke, Bhanu Srikanth Introduction to Content Hub’s Media Production Suite At Netflix, we want to provide filmmakers with the tools they need to produce content at a global scale, with quick turnaround and choice from an extraordinary variety of cameras, formats, workflows, and collaborators. Every series or film …

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Building Workforce AI Agents with Visier and Amazon Quick

Employees across every function are expected to make faster, better-informed decisions, but the information that they need rarely lives in one place. Workforce intelligence (who is in your organization, how they are performing, and where the gaps are) is one of the most valuable signals an enterprise has, and platforms like Visier are purpose-built to …

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Day 2 at Google Cloud Next: A marathon developer keynote

At Google Cloud, every day is Developer Day, but none so much as day 2 of Google Cloud Next, when we hold the developer keynote.  This year’s topic? An in-depth look at Gemini Enterprise Agent Platform. This year’s theme? Planning a marathon for 10,000 participants through the Las Vegas Strip. OK, let’s run with it.  …

Why faster AI isn’t always better

In the race to make AI models not just reason better but respond faster, latency—the delay before an answer appears—is often treated as a purely technical constraint, something to minimize and move past. But how is this relentless push for speed actually impacting the people using these systems every day?

Closed-source AI hate is understandable, but local AI has nothing that should concern AI haters

Let’s face it, AI is forbidden to be praised or used in pretty much any online community outside of AI-focused sites without mass anger and vitriol in said communities. the same old strawman takes and insults show up pretty much every time someone posts an ai-generated image/video on other subreddits. They always say that AI …

ParaRNN: Large-Scale Nonlinear RNNs, Trainable in Parallel

Recurrent Neural Networks (RNNs) are naturally suited to efficient inference, requiring far less memory and compute than attention-based architectures, but the sequential nature of their computation has historically made it impractical to scale up RNNs to billions of parameters. A new advancement from Apple researchers makes RNN training dramatically more efficient — enabling large-scale training …

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Amazon Quick for marketing: From scattered data to strategic action

Imagine the following scenario: You’re leading marketing campaigns, creating content, or driving demand generation. Your campaigns are scattered and your insights are buried. By the time you’ve pieced together what’s working, the moment to act has already passed. This isn’t a tools problem because you have plenty of those. It’s a connection problem. Your marketing …