Breaking Down Barriers: Scaling Multimodal AI with CuMo

Researchers from Nvidia conducted a study on the impact of retrieval augmentation and context window size on the performance of large language models (LLMs) in various tasks. They found that retrieval augmentation consistently improves LLM performance,…

Post-Training Techniques for Language Models Post-training techniques like instruction tuning and reinforcement learning are crucial for improving language models. Unfortunately, open-source methods often lag behind proprietary models due to unclear training processes and data. This gap…

The quest for clean data for pretraining Large Language Models (LLMs) is formidable amid the cluttered digital realm. Traditional web scrapers struggle to differentiate valuable content, leading to noisy data. NeuScraper, developed by researchers, employs neural…

Amazon SageMaker JumpStart now supports token streaming for large language model (LLM) inference responses. This feature allows users to see the model response output as it is being generated, providing a perception of low latency. Streaming…

Researchers have developed RoboHive, a platform for robot learning, to address the challenges in this field. RoboHive serves as a benchmarking and research tool, offering various learning paradigms and hardware integration. Its key features include a…

Latent Diffusion Models are generative models used in machine learning to capture a dataset’s underlying structure. Researchers at Tsinghua University have introduced LCM-LoRA, a training-free acceleration module that enhances the image generation process. By integrating LCM-LoRA…

NYC Mayor Eric Adams is using AI-generated deepfake technology to make automated robocalls to his city’s residents. The AI creates audio of Adams speaking in various languages, allowing him to reach a wider audience. While practical,…

Practical Solutions and Value of Generative Modeling in Molecular Dynamics Overview: Molecular dynamics (MD) is essential for studying molecular systems at the atomic level. However, it can be computationally expensive. Generative modeling offers a solution to…

Practical AI Solutions with Automorphic Solution Offered by Automorphic Automorphic provides a platform that enables developers to easily create and enhance personalized, fine-tuned language models (LLMs) using raw data. This process can be completed in a…

AI’s effectiveness heavily relies on data availability for training purposes. However, a study by University of Toronto Engineering researchers suggests that deep learning models may not always require a lot of training data. The researchers found…

Enhancing Language Models with Self-Reasoning Framework Practical Solutions and Value Retrieval-Augmented Language Model (RALM) integrates external knowledge to reduce factual inaccuracies and enhance response accuracy. A self-reasoning framework by Baidu Inc. aims to improve reliability and…

Introduction to Large Language Models Large language models (LLMs) are essential for many AI systems, driving progress in natural language processing (NLP), computer vision, and scientific research. However, they have challenges, particularly in size and cost.…

The article discusses Self-RAG, a method that improves upon the standard Retrieval Augmented Generation (RAG) architecture. Self-RAG uses fine-tuned language models to determine the relevance of a context and generates special tokens accordingly. It outperforms other…

OpenAI’s CEO, Sam Altman, is orchestrating a staggering funding initiative to raise between $5-7 trillion. This investment aims to expand high-performance AI hardware production to address the skyrocketing demand. Altman is engaging potential investors and government…

GraphRAG: Enhancing AI with Graph Structures Revolutionizing AI with Large Language Models Large Language Models (LLMs) like GPT-4, Qwen2, and LLaMA have revolutionized artificial intelligence, particularly in natural language processing. These models have shown remarkable capabilities…