ML13269 Ultracluster

Scaling Large Language Model (LLM) training with Amazon EC2 Trn1 UltraClusters

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that spans multiple nodes (instances). In October 2022, we launched Amazon EC2 …

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8 ways to cut costs and drive profits using data and AI

We are increasingly seeing one question arise in virtually every customer conversation: How can the organization save costs and drive new revenue streams?  Everyone would love a crystal ball, but what you may not realize is that you already have one. It’s in your data. By leveraging Data Cloud and AI solutions, you can put …

UK’s Conservation AI Makes Huge Leap Detecting Threats to Endangered Species Across the Globe

The video above represents one of the first times that a pangolin, one of the world’s most critically endangered species, was detected in real time using artificial intelligence. A U.K.-based nonprofit called Conservation AI made this possible with the help of NVIDIA technology. Such use of AI can help track even the rarest, most reclusive …

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What Stories Drive the Most Successful Digital Marketing Campaigns?

Have you ever made a purchase that you had to rationalize? Maybe it was very expensive. Or you didn’t really need it. Or it was reasonably priced and you needed it, but you had more urgent expenses. Whether the item was a new car, or a new phone, or a new pair of shoes, it’s …

Improving Human Annotation Effectiveness for Fact Collection by Identifying the Most Relevant Answers

This paper was accepted at the Workshops on Data Science with Human in the Loop at EMNLP 2022 Identifying and integrating missing facts is a crucial task for knowledge graph completion to ensure robustness towards downstream applications such as question answering. Adding new facts to a knowledge graph in real world system often involves human …

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FriendlyCore: A novel differentially private aggregation framework

Posted by Haim Kaplan and Yishay Mansour, Research Scientists, Google Research Differential privacy (DP) machine learning algorithms protect user data by limiting the effect of each data point on an aggregated output with a mathematical guarantee. Intuitively the guarantee implies that changing a single user’s contribution should not significantly change the output distribution of the …