​[[{“value”:” In the rapidly evolving world of AI, the ability to customize language models for specific industries has become more important. Although large language models (LLMs) are adept at handling a wide range of tasks with natural language, they excel at general purpose tasks as… Read More »Customize small language models on AWS with automotive terminology Bruno Pistone AWS Machine Learning Blog

​[[{“value”:” In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails. Amazon Bedrock is a fully managed service that makes foundation models (FMs) from… Read More »Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails Manu Mishra AWS Machine Learning Blog

​[[{“value”:” According to the National Cancer Institute, a cancer biomarker is a “biological molecule found in blood, other body fluids, or tissues that is a sign of a normal or abnormal process, or of a condition or disease such as cancer.” Biomarkers typically differentiate an… Read More »Accelerate analysis and discovery of cancer biomarkers with Amazon Bedrock Agents Hasan Poonawala AWS Machine Learning Blog

​[[{“value”:” As companies of all sizes continue to build generative AI applications, the need for robust governance and control mechanisms becomes crucial. With the growing complexity of generative AI models, organizations face challenges in maintaining compliance, mitigating risks, and upholding ethical standards. This is where… Read More »Automate building guardrails for Amazon Bedrock using test-driven development Harsh Patel AWS Machine Learning Blog

​[[{“value”:” Posted by Jason Jabbour, Kai Kleinbard and Vijay Janapa Reddi (Harvard University) Everyone wants to do the modeling work, but no one wants to do the engineering. If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists designing… Read More »MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering noreply@blogger.com (TensorFlow Blog) The TensorFlow Blog

​[[{“value”:” Planning and decision-making in complex, partially observed environments is a significant challenge in embodied AI. Traditionally, embodied agents rely on physical exploration to gather more information, which can be time-consuming and impractical, especially in large-scale, dynamic environments. For instance, autonomous driving or navigation in… Read More »John Hopkins Researchers Introduce Genex: The AI Model that Imagines its Way through 3D Worlds Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Using large language models (LLMs) has revolutionized artificial intelligence applications, enabling breakthroughs in natural language processing tasks like conversational AI, content generation, and automated code completion. Often with billions of parameters, these models rely on massive memory resources to store intermediate computation states and… Read More »This AI Paper from UC Berkeley Introduces Pie: A Machine Learning Framework for Performance-Transparent Swapping and Adaptive Expansion in LLM Inference Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Log-based anomaly detection has become essential for improving software system reliability by identifying issues from log data. However, traditional deep learning methods often struggle to interpret the semantic details in log data, typically in natural language. LLMs, like GPT-4 and Llama 3, have shown… Read More »LogLLM: Leveraging Large Language Models for Enhanced Log-Based Anomaly Detection Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” One popular term encountered in generative AI practice is retrieval-augmented generation (RAG). Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. The haphazard results may be… Read More »Unbundling the Graph in GraphRAG Paco Nathan AI & ML – Radar

​[[{“value”:” Effective lesson structuring remains a critical challenge in educational settings, particularly when conversations and tutoring sessions need to address predefined topics or worksheet problems. Educators face the complex task of optimally allocating time across different problems while accommodating diverse student learning needs. This challenge… Read More »Stanford Researchers Propose ‘POSR’: A Unique AI Framework for Analyzing Educational Conversations Using Joint Segmentation and Retrieval Mohammad Asjad Artificial Intelligence Category – MarkTechPost