Unstructured, a major innovator in data transformation, has launched the Unstructured Serverless API, a breakthrough solution designed to streamline the processing and preparation of enterprise-level data for artificial intelligence (AI) applications. Not only does this offer a more straightforward approach, but it significantly accelerates the process and reduces costs. The Unstructured Serverless API is a…
Artificial Analysis has launched the Artificial Analysis Text to Image Leaderboard & Arena, an initiative aimed at evaluating the effectiveness of AI image models. The initiative compares open-source and proprietary models, seeking to rate their effectiveness and accuracy based on the preferences of humans. The leaderboard, updated with ELO scores compiled from over 45,000 human…
Researchers from the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Clinical Center, and National Center for Biotechnology Information have introduced a new method for creating synthetic X-ray images using data from computed tomography (CT) scans. The method, called Digitally Reconstructed Radiography (DRR), uses ray tracing techniques to simulate the path of X-rays through CT volumes. Unlike…
MARS5 TTS, an open-source text-to-speech system, has been released by the team at Camb AI, offering game-changing levels of precision and control in the field of speech synthesis. This innovative system can clone voices and provide nuanced control of prosody using less than 5 seconds of audio input.
MARS5 TTS utilises a two-step process involving a…
The use of large language models (LLMs), such as ChatGPT, has significantly increased in academic writing, resulting in observable shifts in writing style and vocabulary, particularly in biomedical research. Concerns have risen around the authenticity and originality of scientific content and its implications for research integrity and the evaluation of academic contributions.
Traditional methods for detecting…
Sleep monitoring is a crucial part of maintaining overall health, yet accurately assessing sleep and diagnosing disorders is a complex task due to the need for multi-modal data interpretation typically obtained through polysomnography (PSG). Current methods often depend on extensive manual evaluation by trained technicians, making them time-consuming and susceptible to variability. To address these…
The technological world is advancing at a rapid pace, making the management of complex tasks more challenging. The difficulty lies in breaking down extensive objectives into manageable parts and coordinating multiple processes to achieve a unified result, a challenge that becomes more significant when using AI models, which can sometimes yield fragmented or incomplete results.
Traditional…
Retrieval-Augmented Generation (RAG) methods improve the ability of large language models (LLMs) by incorporating external knowledge gleaned from vast data sets. These methods are particularly useful for open-domain question answering where detailed and accurate answers are needed. RAG systems can utilize external information to complement the inherent knowledge built into LLMs, making them more effective…
NuMind has unveiled NuExtract, a revolutionary text-to-JSON language model that represents a significant enhancement in structured data extraction from text, aiming to efficiently transform unstructured text into structured data.
NuExtract significantly distinguishes itself from its competitors through its innovative design and training methods, providing exceptional performance while maintaining cost-efficacy. It is designed to interact efficiently…
Google's Project Zero research team is leveraging Large Language Models (LLMs) to improve cybersecurity and identify elusive 'unfuzzable' vulnerabilities. These are flaws that evade detection by conventional automated systems and often go undetected until they're exploited.
LLMs replicate the analytical prowess of human experts, identifying these vulnerabilities through extensive reasoning processes. To optimize LLMs use, the…
Large language models (LLMs) and latent variable models (LVMs) can present significant challenges during deployment, such as balancing low inference overhead and the rapid change of adapters. Traditional methods, such as Low Rank Adaptation (LoRA), often result in increased latency or loss of rapid switching capabilities. This can prove particularly problematic in resource-constrained settings like…