Part 1: Creating the Source of Truth for Impressions By: Tulika Bhatt Imagine scrolling through Netflix, where each movie poster or promotional banner competes for your attention. Every image you hover over isn’t just a visual placeholder; it’s a critical data point that fuels our sophisticated personalization engine. At Netflix, we call these images ‘impressions,’ and …
Artificial Intelligence (AI) and large language models (LLMs) are experiencing explosive growth, powering applications from machine translation to artistic creation. These technologies rely on intensive computations that require specialized hardware resources, like GPUs. But access to GPUs can be challenging, both in terms of availability and cost. For Google Cloud users, the introduction of Dynamic …
Read more “With MultiKueue, grab GPUs for your GKE cluster, wherever they may be”
Humanoid robots have significant gaps in their sensing and perception, making it hard to perform motion planning in dense environments. To address this, we introduce ARMOR, a novel egocentric perception system that integrates both hardware and software, specifically incorporating wearable-like depth sensors for humanoid robots. Our distributed perception approach enhances the robot’s spatial awareness, and …
Read more “ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning”
AI agents continue to gain momentum, as businesses use the power of generative AI to reinvent customer experiences and automate complex workflows. We are seeing Amazon Bedrock Agents applied in investment research, insurance claims processing, root cause analysis, advertising campaigns, and much more. Agents use the reasoning capability of foundation models (FMs) to break down …
Read more “Build a dynamic, role-based AI agent using Amazon Bedrock inline agents”
Generative AI is now well beyond the hype and into the realm of practical application. But while organizations are eager to build enterprise-ready gen AI solutions on top of large language models (LLMs), they face challenges in managing, securing, and scaling these deployments, especially when it comes to APIs. As part of the platform team, …
Ibrahim Said Ahmad†, Antonios Anastasopoulos††††, Ondřej Bojar¶, Claudia Borg††, Marine Carpuat‡, Roldano Cattoni§, Mauro Cettolo§, William Chen‡‡, Qianqian Dong¶¶, Marcello Federico§§, Barry Haddow‡‡‡, Dávid Javorsky¶, Mateusz Krubiński¶, Tsz Kin Lam‡‡‡, Xutai Ma‡‡§, Prashant Mathur§§, Evgeny Matusov¶¶¶, Chandresh Kumar Maurya¶¶†, John P. McCrae†††, Kenton Murray†††, Satoshi Nakamura§§§, Matteo Negri§, Jan Niehues††¶, Xing Niu§§, Atul Kr. Ojha†††, …
There’s a growing demand from customers to incorporate generative AI into their businesses. Many use cases involve using pre-trained large language models (LLMs) through approaches like Retrieval Augmented Generation (RAG). However, for advanced, domain-specific tasks or those requiring specific formats, model customization techniques such as fine-tuning are sometimes necessary. Amazon Bedrock provides you with the …
Read more “Fine-tune LLMs with synthetic data for context-based Q&A using Amazon Bedrock”
Editor’s note: This article, originally published on March 13, 2023, has been updated. The mics were live and tape was rolling in the studio where the Miles Davis Quintet was recording dozens of tunes in 1956 for Prestige Records. When an engineer asked for the next song’s title, Davis shot back, “I’ll play it, and …
This blog post is co-written with George Orlin from Meta. Today, we are excited to announce that Meta’s Segment Anything Model (SAM) 2.1 vision segmentation model is publicly available through Amazon SageMaker JumpStart to deploy and run inference. Meta SAM 2.1 provides state-of-the-art video and image segmentation capabilities in a single model. This cutting-edge model …
Read more “Meta SAM 2.1 is now available in Amazon SageMaker JumpStart”
*Primary Contributors Attention is a key part of the transformer architecture. It is a sequence-to-sequence mapping that transforms each sequence element into a weighted sum of values. The weights are typically obtained as the softmax of dot products between keys and queries. Recent work has explored alternatives to softmax attention in transformers, such as ReLU …
Read more “Theory, Analysis, and Best Practices for Sigmoid Self-Attention”