zetabyte

​[[{“value”:” Enhancing the reasoning abilities of LLMs by optimizing test-time compute is a critical research challenge. Current approaches primarily rely on fine-tuning models with search traces or RL using binary outcome rewards. However, these methods may not fully exploit test-time compute efficiently. Recent research suggests… Read More »Optimizing Test-Time Compute for LLMs: A Meta-Reinforcement Learning Approach with Cumulative Regret Minimization Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Computer use is a breakthrough capability from Anthropic that allows foundation models (FMs) to visually perceive and interpret digital interfaces. This capability enables Anthropic’s Claude models to identify what’s on a screen, understand the context of UI elements, and recognize actions that should be… Read More »Getting started with computer use in Amazon Bedrock Agents Eashan Kaushik AWS Machine Learning Blog

​[[{“value”:” Organizations building and deploying AI applications, particularly those using large language models (LLMs) with Retrieval Augmented Generation (RAG) systems, face a significant challenge: how to evaluate AI outputs effectively throughout the application lifecycle. As these AI technologies become more sophisticated and widely adopted, maintaining… Read More »Evaluating RAG applications with Amazon Bedrock knowledge base evaluation Ishan Singh AWS Machine Learning Blog

​[[{“value”:” Advancements in multimodal large language models have enhanced AI’s ability to interpret and reason about complex visual and textual information. Despite these improvements, the field faces persistent challenges, especially in mathematical reasoning tasks. Traditional multimodal AI systems, even those with extensive training data and… Read More »MMR1-Math-v0-7B Model and MMR1-Math-RL-Data-v0 Dataset Released: New State of the Art Benchmark in Efficient Multimodal Mathematical Reasoning with Minimal Data Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Recent advancements in embedding models have focused on transforming general-purpose text representations for diverse applications like semantic similarity, clustering, and classification. Traditional embedding models, such as Universal Sentence Encoder and Sentence-T5, aimed to provide generic text representations, but recent research highlights their limitations in… Read More »Google AI Introduces Gemini Embedding: A Novel Embedding Model Initialized from the Powerful Gemini Large Language Model Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” This post was co-written with Vishal Singh, Data Engineering Leader at Data & Analytics team of GoDaddy Generative AI solutions have the potential to transform businesses by boosting productivity and improving customer experiences, and using large language models (LLMs) in these solutions has become… Read More »How GoDaddy built a category generation system at scale with batch inference for Amazon Bedrock Vishal Singh AWS Machine Learning Blog

​[[{“value”:” Posted by the TensorFlow team TensorFlow 2.19 has been released! Highlights of this release include changes to the C++ API in LiteRT, bfloat16 support for tflite casting, discontinue of releasing libtensorflow packages. Learn more by reading the full release notes. Note: Release updates on… Read More »What’s new in TensorFlow 2.19 noreply@blogger.com (TensorFlow Blog) The TensorFlow Blog

​[[{“value”:” Open foundation models (FMs) allow organizations to build customized AI applications by fine-tuning for their specific domains or tasks, while retaining control over costs and deployments. However, deployment can be a significant portion of the effort, often requiring 30% of project time because engineers… Read More »Benchmarking customized models on Amazon Bedrock using LLMPerf and LiteLLM Felipe Lopez AWS Machine Learning Blog