AI

With AI, researchers predict the location of virtually any protein within a human cell

A protein located in the wrong part of a cell can contribute to several diseases, such as Alzheimer’s, cystic fibrosis, and cancer. But there are about 70,000 different proteins and protein variants in a single human cell, and since scientists can typically only test for a handful in one experiment, it is extremely costly and […]

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Cost-effective AI image generation with PixArt-Σ inference on AWS Trainium and AWS Inferentia

PixArt-Sigma is a diffusion transformer model that is capable of image generation at 4k resolution. This model shows significant improvements over previous generation PixArt models like Pixart-Alpha and other diffusion models through dataset and architectural improvements. AWS Trainium and AWS Inferentia are purpose-built AI chips to accelerate machine learning (ML) workloads, making them ideal for

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Customize DeepSeek-R1 671b model using Amazon SageMaker HyperPod recipes – Part 2

This post is the second part of the DeepSeek series focusing on model customization with Amazon SageMaker HyperPod recipes (or recipes for brevity). In Part 1, we demonstrated the performance and ease of fine-tuning DeepSeek-R1 distilled models using these recipes. In this post, we use the recipes to fine-tune the original DeepSeek-R1 671b parameter model.

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Build a financial research assistant using Amazon Q Business and Amazon QuickSight for generative AI–powered insights

According to a Gartner survey in 2024, 58% of finance functions have adopted generative AI, marking a significant rise in adoption. Among these, four primary use cases have emerged as especially prominent: intelligent process automation, anomaly detection, analytics, and operational assistance. In this post, we show you how Amazon Q Business can help augment your

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Study shows vision-language models can’t handle queries with negation words

Imagine a radiologist examining a chest X-ray from a new patient. She notices the patient has swelling in the tissue but does not have an enlarged heart. Looking to speed up diagnosis, she might use a vision-language machine-learning model to search for reports from similar patients. But if the model mistakenly identifies reports with both

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MIT Department of Economics to launch James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work

Starting in July, MIT’s Shaping the Future of Work Initiative in the Department of Economics will usher in a significant new era of research, policy, and education of the next generation of scholars, made possible by a gift from the James M. and Cathleen D. Stone Foundation. In recognition of the gift and the expansion

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