Retrieval-augmented generation (RAG) supercharges large language models (LLMs) by connecting them to real-time, proprietary, and specialized data. This helps LLMs deliver more accurate, relevant, and contextually aware responses, minimizing hallucinations and building trust in AI applications. But RAG can be a double-edged sword: while the concept is straightforward – find relevant information and feed it …
Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations
Teleoperation for robot imitation learning is bottlenecked by hardware availability. Can high-quality robot data be collected without a physical robot? We present a system for augmenting Apple Vision Pro with real-time virtual robot feedback. By providing users with an intuitive understanding of how their actions translate to robot motions, we enable the collection of natural …
Read more “ARMADA: Augmented Reality for Robot Manipulation and Robot-Free Data Acquisition”
This article is the first in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. We kick off with a few topics focused on how we’re empowering Netflix to efficiently produce and effectively deliver high quality, actionable analytic insights across the …
Read more “Part 1: A Survey of Analytics Engineering Work at Netflix”
Developers face significant challenges when using foundation models (FMs) to extract data from unstructured assets. This data extraction process requires carefully identifying models that meet the developer’s specific accuracy, cost, and feature requirements. Additionally, developers must invest considerable time optimizing price performance through fine-tuning and extensive prompt engineering. Managing multiple models, implementing safety guardrails, and …
Read more “Simplify multimodal generative AI with Amazon Bedrock Data Automation”
One of the biggest areas of promise for generative AI is coding assistance — leveraging the power of large language models to help developers create or update application code with amazing speed and accuracy, dramatically boosting productivity. For Gemini Code Assist, 2024 has been a year of tremendous growth and innovation, as we help companies …
Read more “Reach beyond the IDE with tools for Gemini Code Assist”
We’re rolling out a new, state-of-the-art video model, Veo 2, and updates to Imagen 3. Plus, check out our new experiment, Whisk.
Inside Look: The Baseline Team and Forward-Deployed Infrastructure Engineering at Palantir At Palantir, our customers rely on our applications operating seamlessly across a variety of cloud providers, on-premises hardware, and both commercial and government networks. They need our platforms to function reliably in these diverse environments. This is where we, the Forward Deployed Infrastructure Engineering team — known …
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Today, we are excited to announce that the Llama 3.3 70B from Meta is available in Amazon SageMaker JumpStart. Llama 3.3 70B marks an exciting advancement in large language model (LLM) development, offering comparable performance to larger Llama versions with fewer computational resources. In this post, we explore how to deploy this model efficiently on …
Read more “Llama 3.3 70B now available in Amazon SageMaker JumpStart”
AI is rapidly transforming industries and redefining the future of work. However, many organizations face a significant hurdle: bridging the knowledge gap and acquiring the necessary skills to effectively harness the power of AI. Recognizing this challenge, Google Cloud is set to launch the AI Playground in Shoreditch, Central London, in the first quarter of …
Read more “AI Playground: Where learning and innovation converge in the heart of London”