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Meta SAM 2.1 is now available in Amazon SageMaker JumpStart

This blog post is co-written with George Orlin from Meta. Today, we are excited to announce that Meta’s Segment Anything Model (SAM) 2.1 vision segmentation model is publicly available through Amazon SageMaker JumpStart to deploy and run inference. Meta SAM 2.1 provides state-of-the-art video and image segmentation capabilities in a single model. This cutting-edge model …

Theory, Analysis, and Best Practices for Sigmoid Self-Attention

*Primary Contributors Attention is a key part of the transformer architecture. It is a sequence-to-sequence mapping that transforms each sequence element into a weighted sum of values. The weights are typically obtained as the softmax of dot products between keys and queries. Recent work has explored alternatives to softmax attention in transformers, such as ReLU …

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Transforming credit decisions using generative AI with Rich Data Co and AWS

This post is co-written with Gordon Campbell, Charles Guan, and Hendra Suryanto from RDC.  The mission of Rich Data Co (RDC) is to broaden access to sustainable credit globally. Its software-as-a-service (SaaS) solution empowers leading banks and lenders with deep customer insights and AI-driven decision-making capabilities. Making credit decisions using AI can be challenging, requiring …

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Networking support for AI workloads

At Google Cloud, we strive to make it easy to deploy AI models onto our infrastructure. In this blog we explore how the Cross-Cloud Network solution supports your AI workloads. Managed and Unmanaged AI options Google Cloud provides both managed (Vertex AI) and do-it-yourself (DIY) approaches for running AI workloads.  Vertex AI: A fully managed …

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AI-Designed Proteins Take on Deadly Snake Venom

Every year, venomous snakes kill over 100,000 people and leave 300,000 more with devastating injuries — amputations, paralysis and permanent disabilities. The victims are often farmers, herders and children in rural communities across sub-Saharan Africa, South Asia and Latin America. For them, a snakebite isn’t just a medical crisis — it’s an economic catastrophe. Treatment …

Cut Your Losses in Large-Vocabulary Language Models

As language models grow ever larger, so do their vocabularies. This has shifted the memory footprint of LLMs during training disproportionately to one single layer: the cross-entropy in the loss computation. Cross-entropy builds up a logit matrix with entries for each pair of input tokens and vocabulary items and, for small models, consumes an order …

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Requirements for AI in Production in Insurance Underwriting

Introduction Large language models present both massive opportunities and significant complexities for the insurance industry. Insurers can use AI to increase operational efficiency, improve the accuracy of underwriting decisions, enhance customer experience, and more effectively coordinate with partners. Yet in a heavily-regulated industry like insurance, ensuring objectivity and the appropriate level of human oversight in …

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Governing the ML lifecycle at scale, Part 4: Scaling MLOps with security and governance controls

Data science teams often face challenges when transitioning models from the development environment to production. These include difficulties integrating data science team’s models into the IT team’s production environment, the need to retrofit data science code to meet enterprise security and governance standards, gaining access to production grade data, and maintaining repeatability and reproducibility in …

News you can use: What we announced in AI this month

2025 is off to a racing start. From announcing strides in the new Gemini 2.0 model family to retailers accelerating with Cloud AI, we spent January investing in our partner ecosystem, open-source, and ways to make AI more useful. We’ve heard from people everywhere, from developers to CMOs, about the pressure to adapt the latest …