Category: FAANG

Posted by Tapio Schneider, Visiting Researcher, and Yi-fan Chen, Engineering Lead, Google Research Today’s climate models successfully capture broad global warming trends. However, because of uncertainties about processes that are small in scale yet globally important, such as clouds and ocean turbulence, these models’ predictions of upcoming climate changes are not very accurate in detail. …

Read more “Improving simulations of clouds and their effects on climate”

This is a guest post by A.K Roy from Qualcomm AI. Amazon Elastic Compute Cloud (Amazon EC2) DL2q instances, powered by Qualcomm AI 100 Standard accelerators, can be used to cost-efficiently deploy deep learning (DL) workloads in the cloud. They can also be used to develop and validate performance and accuracy of DL workloads that …

Read more “Amazon EC2 DL2q instance for cost-efficient, high-performance AI inference is now generally available”

Posted by Dave Hawkey, Software Engineer, Google Research Two years ago we announced Project Guideline, a collaboration between Google Research and Guiding Eyes for the Blind that enabled people with visual impairments (e.g., blindness and low-vision) to walk, jog, and run independently. Using only a Google Pixel phone and headphones, Project Guideline leverages on-device machine …

Read more “Open sourcing Project Guideline: A platform for computer vision accessibility technology”

Today, the NHS has chosen Palantir, supported by a group of companies including Accenture, PwC, NECS and Carnall Farrar, to help deliver a Federated Data Platform (FDP). AWS and Microsoft will provide cloud platform services. The FDP will improve patient care by bringing together the information needed to plan and deliver care, and reduce the …

Read more “Palantir and the NHS”

by Jun He, Yingyi Zhang, and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset. This not only reduces the cost of compute resources but …

Read more “Incremental Processing using Netflix Maestro and Apache Iceberg”

Posted by Dave Hawkey, Software Engineer, Google Research Two years ago we announced Project Guideline, a collaboration between Google Research and Guiding Eyes for the Blind that enabled people with visual impairments (e.g., blindness and low-vision) to walk, jog, and run independently. Using only a Google Pixel phone and headphones, Project Guideline leverages on-device machine …

Read more “Open sourcing Project Guideline: A platform for computer vision accessibility technology”

In the dynamic world of streaming on Amazon Music, every search for a song, podcast, or playlist holds a story, a mood, or a flood of emotions waiting to be unveiled. These searches serve as a gateway to new discoveries, cherished experiences, and lasting memories. The search bar is not just about finding a song; …

Read more “How Amazon Music uses SageMaker with NVIDIA to optimize ML training and inference performance and cost”

Retrieval Augmented Generation (RAG) allows you to provide a large language model (LLM) with access to data from external knowledge sources such as repositories, databases, and APIs without the need to fine-tune it. When using generative AI for question answering, RAG enables LLMs to answer questions with the most relevant, up-to-date information and optionally cite …

Read more “Use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation”