In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs
EnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM. It could help coders work with AI agents more efficiently.
Associa collaborated with the AWS Generative AI Innovation Center to build a generative AI-powered document classification system aligning with Associa’s long-term vision of using generative AI to achieve operational efficiencies in document management. The solution automatically categorizes incomin
Torralba’s research focuses on computer vision, machine learning, and human visual perception.
Building upon our earlier work of marketing campaign image generation using Amazon Nova foundation models, in this post, we demonstrate how to enhance image generation by learning from previous marketing campaigns. We explore how to integrate Amazon Bedrock, AWS Lambda, and Amazon OpenSearch Serverl
In this post, we illustrate how Clarus Care, a healthcare contact center solutions provider, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a generative AI-powered contact center prototype. This solution enables conversational interaction and multi-intent resolution th
MIT researchers’ DiffSyn model offers recipes for synthesizing new materials, enabling faster experimentation and a shorter journey from hypothesis to use.
As AI technology advances, a new interdisciplinary course seeks to equip students with foundational critical thinking skills in computing.
Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earli
Self supervised learning (SSL) is a machine learning paradigm where models learn to understand the underlying structure of data without explicit supervision from labeled samples. The acquired representations from SSL have demonstrated useful for many downstream tasks including clustering, and linear
