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RAD Lab AlphaFold module for researchers

Understanding protein geometries is essential to understanding a protein’s function, and thus essential to a range of disruptive research projects, from creating more sustainable materials to developing more effective treatments to diseases. For this reason, the “protein-folding problem” ranked for decades among biological science’s grand challenges—until Google’s DeepMind developed AlphaFold.   Widely hailed as a breakthrough …

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NVIDIA, Evozyne Create Generative AI Model for Proteins

Using a pretrained AI model from NVIDIA, startup Evozyne created two proteins with significant potential in healthcare and clean energy. A joint paper released today describes the process and the biological building blocks it produced. One aims to cure a congenital disease, another is designed to consume carbon dioxide to reduce global warming. Initial results …

NVIDIA Helps Retail Industry Tackle Its $100 Billion Shrink Problem

The global retail industry has a $100 billion problem. “Shrinkage” — the loss of goods due to theft, damage and misplacement — significantly crimps retailers’ profits. An estimated 65% of shrinkage is due to theft, according to the National Retail Federation’s 2022 Retail Security Survey, conducted in partnership with the Loss Prevention Research Council. And …

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Forecasting Potential Misuses of Language Models for Disinformation Campaigns—and How to Reduce Risk

OpenAI researchers collaborated with Georgetown University’s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and culminated in a co-authored report building on …

How to unlock a scientific approach to change management with powerful data insights

Without a doubt, there is exponential growth in the access to and volume of process data we all, as individuals, have at our fingertips. Coupled with a current climate that is proving to be increasingly ambiguous and complex, there is a huge opportunity to leverage data insights to drive a more robust, evidence-based methodology to …

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Enriching real-time news streams with the Refinitiv Data Library, AWS services, and Amazon SageMaker

This post is co-authored by Marios Skevofylakas, Jason Ramchandani and Haykaz Aramyan from Refinitiv, An LSEG Business. Financial service providers often need to identify relevant news, analyze it, extract insights, and take actions in real time, like trading specific instruments (such as commodities, shares, funds) based on additional information or context of the news item. …

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Reading and storing data for custom model training on Vertex AI

Before you can train ML models in the cloud, you need to get your data to the cloud.  But when it comes to storing data on Google Cloud there are a lot of different options. Not to mention the different ways you can read in data when designing input pipelines for custom models. Should you …

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How to use Netezza Performance Server query data in Amazon Simple Storage Service (S3)

In this example, we will demonstrate using current data within a Netezza Performance Server as a Service (NPSaaS) table combined with historical data in Parquet files to determine if flight delays have increased in 2022 due to the impact of the COVID-19 pandemic on the airline travel industry. This demonstration illustrates how Netezza Performance Server …

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Best practices for load testing Amazon SageMaker real-time inference endpoints

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so …

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Sparse Features Support in BigQuery

Introduction Most machine learning models require the input features to be in numerical format and if the features are in categorial format, pre-processing steps such as one-hot encoding are needed to convert them into numerical format. Converting a large number of categorical values may lead to creating sparse features, a set of features that contains …