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.  …

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 …

Apple Machine Learning Research at ICLR 2026

Apple is advancing AI and ML with fundamental research, much of which is shared through publications and engagement at conferences in order to accelerate progress in this important field and support the broader community. This week, the Fourteenth International Conference on Learning Representations (ICLR) will be held in Rio de Janeiro, Brazil, and Apple is …

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Frontend Engineering at Palantir: Engineering Multilingual Collaboration

Frontend Engineering at Palantir: Building Multilingual Collaboration About this SeriesFrontend engineering at Palantir goes far beyond building standard web apps. Our engineers design interfaces for mission-critical decision-making, build operational applications that translate insight to action, and create systems that handle massive datasets — thinking not just about what the user needs, but what they need when the …

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Cost-effective multilingual audio transcription at scale with Parakeet-TDT and AWS Batch

Many organizations are archiving large media libraries, analyzing contact center recordings, preparing training data for AI, or processing on-demand video for subtitles. When data volumes grow significantly, managed automatic speech recognition (ASR) service costs can quickly become the primary constraint on scalability. To address this cost-scalability challenge, we use the NVIDIA Parakeet-TDT-0.6B-v3 model, deployed through …