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Document AI Custom Extractor, powered by gen AI, is now Generally Available

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Today, we are excited to announce that the Document AI Custom Extractor, powered by generative AI, is Generally Available (GA), open to all customers, and ready for production use through APIs and the Google Cloud Console. The Custom Extractor, built with Google’s foundation models, helps parse data from structured and unstructured documents quickly and with …

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How Generative AI Is Redefining the Retail Industry

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Ninety-eight percent of retailers plan to invest in generative AI in the next 18 months, according to a new survey conducted by NVIDIA. That makes retail one of the industries racing fastest to adopt generative AI to ramp up productivity, transform customer experiences and improve efficiency. Early deployments in the retail industry include personalized shopping …

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AI helps whittle down candidates for hydrogen carriers in liquid form from billions to about 40

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In a computational study leveraging artificial intelligence (AI), scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have assessed 160 billion molecules, a number exceeding the people born in the entire span of human history. Their goal was to screen the molecules for suitability as liquid carriers of hydrogen.

The importance of data ingestion and integration for enterprise AI

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The emergence of generative AI prompted several prominent companies to restrict its use because of the mishandling of sensitive internal data. According to CNN, some companies imposed internal bans on generative AI tools while they seek to better understand the technology and many have also blocked the use of internal ChatGPT. Companies still often accept …

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Inference Llama 2 models with real-time response streaming using Amazon SageMaker

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With the rapid adoption of generative AI applications, there is a need for these applications to respond in time to reduce the perceived latency with higher throughput. Foundation models (FMs) are often pre-trained on vast corpora of data with parameters ranging in scale of millions to billions and beyond. Large language models (LLMs) are a …

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Getting started with AI notebooks and Jupyterhub on GKE

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The recent growth in distributed, compute-intensive ML applications has prompted data scientists and ML practitioners to find easy ways to prototype and develop their ML models. Running your Jupyter notebooks and JupyterHub on Google Kubernetes Engine (GKE) can provide a way to run your solution with security and scalability built-in as core elements of the …

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