NVEagle Released by NVIDIA: A Super Impressive Vision Language Model that Comes in 7B, 13B, and 13B Fine-Tuned on Chat

Discover cutting-edge Dimensionality Reduction techniques to enhance the performance of your Machine Learning models. Find more detailed information on this topic in the Towards Data Science article.

Mistral AI Introduces Mistral Saba A New Language Model for Arabic and Tamil As AI technology grows, one major challenge is creating models that understand the variety of human languages, especially regional dialects and cultural contexts.…

Artificial Intelligence and Deep Learning have enabled Scientific Machine Learning (SciML), a new field combining classic PDE-based modeling and machine learning. It consists of PDE solvers, PDE discovery, and operator learning, addressing dynamic systems and PDEs…

A UC Berkeley research team has developed a novel LM pipeline, a retrieval-augmented language model system designed to improve forecasting accuracy. The system utilizes web-scale data and rapid parsing capabilities of language models, achieving a Brier…

Scientists from Bar-Ilan University explore the necessity of deep learning in AI and propose alternative machine learning techniques for intricate classification tasks, while continuing their studies on tree-like architectures.

Recently, a paper on the use of audio-visual synchronization for learning audio-visual representations was accepted at the Machine Learning for Audio Workshop at NeurIPS 2023. The paper discusses the effectiveness of unsupervised training frameworks, particularly the…

NetEase Youdao has released an open-source text-to-speech (TTS) engine called “Yi Mo Sheng.” It offers web and script interfaces, allowing for batch result generation, making it suitable for applications requiring emotional synthesis of voices. The engine…

Long-form RobustQA Dataset and RAG-QA Arena Practical Solutions and Value Question answering (QA) in natural language processing (NLP) is enhanced by Retrieval-augmented generation (RAG), which filters out irrelevant information and presents only the most pertinent passages…

Enhancing LLM Reasoning with Multi-Attempt Reinforcement Learning Recent advancements in reinforcement learning (RL) for large language models (LLMs), such as DeepSeek R1, show that even simple question-answering tasks can significantly improve reasoning capabilities. Traditional RL methods…

New York University researchers trained an AI system using 60 hours of first-person video recordings from children aged 6 months to 2 years. The AI employed self-supervised learning to understand actions and changes like a child.…

Practical Solutions for Document Conversion with AI Challenges in Document Conversion Converting PDFs to machine-processable formats has been challenging due to the diverse and complex nature of PDF files. This often results in a loss of…

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…

Edge AI and Its Advantages over Traditional AI Edge artificial intelligence (Edge AI) involves implementing AI algorithms and models on local devices like sensors or IoT devices at the network’s periphery. This allows for immediate data…

Addressing High Latency in RAG Systems High latency in time-to-first-token (TTFT) is a major issue for retrieval-augmented generation (RAG) systems. Traditional RAG systems process multiple document chunks to generate responses, which can be slow due to…

Neural MMO 2.0 is an advanced multi-agent environment for reinforcement learning research. It offers a flexible task system that allows users to define diverse objectives and reward signals. The platform has undergone a complete rewrite and…

The KDD conference in Long Beach, CA showcased various topics, but the highlights were Large Language Models (LLMs) and Graph Learning. The LLM Revolution keynote by Ed Chi of Google discussed the ways LLMs are bridging…

Claude.ai, developed by Anthropic, has released an upgraded version called Claude 2.1. The major improvement is the doubling of its context window, now at 200,000 tokens, making it the largest in the industry. While it performs…

Understanding Language Model Routing Language model routing is an emerging area focused on using large language models (LLMs) effectively for various tasks. These models can generate text, summarize information, and reason through data. The challenge is…

Amazon SageMaker has released a new version (0.25.0) of Large Model Inference (LMI) Deep Learning Containers (DLCs) with support for NVIDIA’s TensorRT-LLM Library. This upgrade provides improved performance and efficiency for large language models (LLMs) on…