zach con

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 …

1yFZA7ezVoz2PPt4dxFdI0Q

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 …

ML 19415 image 1

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 …

Can Large Language Models Understand Context?

Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the evaluation of LLMs encompasses various domains within the realm of Natural Language Processing, limited attention has been paid to probing their linguistic capability of understanding contextual features. …

figure1 new

From developer desks to the whole organization: Running Claude Cowork in Amazon Bedrock

Today, we’re excited to announce Claude Cowork in Amazon Bedrock. You can now run Cowork and Claude Code Desktop through Amazon Bedrock, directly or using an LLM gateway. From startups to global enterprises across every industry, organizations build with Claude Code in Amazon Bedrock to boost developer productivity and accelerate delivery. With Amazon Bedrock you …

From keynote to the terminal: Join our Next ‘26 developer livestreams

The main stage at Google Cloud Next is where the vision is set. This year, we’re bridging the gap between those massive “Cloud-scale” announcements and your local terminal. We are thrilled to announce the Next ‘26 developer livestreams, a daily broadcast live from the show floor at Google Cloud Next. We aren’t just reporting the …

What Do Your Logits Know? (The Answer May Surprise You!)

Recent work has shown that probing model internals can reveal a wealth of information not apparent from the model generations. This poses the risk of unintentional or malicious information leakage, where model users are able to learn information that the model owner assumed was inaccessible. Using vision-language models as a testbed, we present the first …

ML 20676 image 1

Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances

As the demand for generative AI continues to grow, developers and enterprises seek more flexible, cost-effective, and powerful accelerators to meet their needs. Today, we are thrilled to announce the availability of G7e instances powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs on Amazon SageMaker AI. You can provision nodes with 1, 2, …