Google Research and ISTA are using light microscopes to “map” the brain.
For more than a decade, Google Research has been using AI to precisely map the connections between every cell in the brain in an endeavor called connectomics. Now, in co…
For more than a decade, Google Research has been using AI to precisely map the connections between every cell in the brain in an endeavor called connectomics. Now, in co…
This post is co-written with Kilian Zimmerer and Daniel Ringler from Deutsche Bahn. Every day, Deutsche Bahn (DB) moves over 6.6 million passengers across Germany, requiring precise time series forecasting for a wide range of purposes. However, building accurate forecasting models traditionally required significant expertise and weeks of development time. Today, we’re excited to explore …
Google.org has announced which organizations will receive the final $10 million in funding from its $75 million AI Opportunity Fund to provide American nonprofits with A…
Amazon Bedrock Guardrails announces the general availability of image content filters, enabling you to moderate both image and text content in your generative AI applications. Previously limited to text-only filtering, this enhancement now provides comprehensive content moderation across both modalities. This new capability removes the heavy lifting required to build your own image safeguards or …
These efforts, including new responsible AI curriculum and a $1 million grant to MIT RAISE, all aim to help students and educators use AI safely and effectively.
When implementing machine learning (ML) workflows in Amazon SageMaker Canvas, organizations might need to consider external dependencies required for their specific use cases. Although SageMaker Canvas provides powerful no-code and low-code capabilities for rapid experimentation, some projects might require specialized dependencies and libraries that aren’t included by default in SageMaker Canvas. This post provides an …
Integrating custom dependencies in Amazon SageMaker Canvas workflows Read More »
In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. Generative AI solutions can play an invaluable role during the model development phase by simplifying training and test data creation for multiclass classification supervised …
Generate training data and cost-effectively train categorical models with Amazon Bedrock Read More »
Amazon Bedrock cross-Region inference capability that provides organizations with flexibility to access foundation models (FMs) across AWS Regions while maintaining optimal performance and availability. However, some enterprises implement strict Regional access controls through service control policies (SCPs) or AWS Control Tower to adhere to compliance requirements, inadvertently blocking cross-Region inference functionality in Amazon Bedrock. This …
Enable Amazon Bedrock cross-Region inference in multi-account environments Read More »
Amazon SageMaker JumpStart is a machine learning (ML) hub that provides pre-trained models, solution templates, and algorithms to help developers quickly get started with machine learning. Within SageMaker JumpStart, the private model hub feature allows organizations to create their own internal repository of ML models, enabling teams to share and manage models securely within their …
Amazon SageMaker JumpStart adds fine-tuning support for models in a private model hub Read More »
In the competitive world of game development, staying ahead of technological advancements is crucial. Generative AI has emerged as a game changer, offering unprecedented opportunities for game designers to push boundaries and create immersive virtual worlds. At the forefront of this revolution is Stability AI’s cutting-edge text-to-image AI model, Stable Diffusion 3.5 Large (SD3.5 Large), …