The use of pretrained language models and their creative applications have contributed to significant improvements in the quality of information retrieval (IR). However, there are questions about the necessity and efficiency of training these models on large datasets, especially for languages with scant labeled IR data or niche domains.
Researchers from the University of Waterloo,…
Replete AI has launched Replete-Coder-Qwen2-1.5b, an artificial intelligence (AI) model with extensive capabilities in coding and other areas. Developed using a mix of non-coding and coding data, the model is designed to perform diverse tasks, making it a versatile solution for a range of applications.
Replete-Coder-Qwen2-1.5b is part of the Replete-Coder series and has been…
Natural evolution has meticulously shaped proteins over more than three billion years. Modern-day research is closely studying these proteins to understand their structures and functions. Large language models are increasingly being employed to interpret the complexities of these protein structures. Such models demonstrate a solid capacity, even without specific training on biological functions, to naturally…
Scientists from Evolutionary Scale PBC, Arc Institute, and the University of California have developed an advanced generative language model for proteins known as ESM3. The protein language model is a sophisticated tool designed to understand and forecast proteins' sequence, structure, and function. It applies the masked language modeling approach to predict masked portions of protein…
Researchers have been focusing on an effective method to leverage in-context learning in transformer-based models like GPT-3+. Despite their success in enhancing AI performance, the method's functionality remains partially understood. In light of this, a team of researchers from the University of California, Los Angeles (UCLA) examined the factors affecting in-context learning. They found that…
Advanced language models such as GPT-3+ have shown significant improvements in performance by predicting the succeeding word in a sequence using more extensive datasets and larger model capacity. A key characteristic of these transformer-based models, aptly named as "in-context learning," allows the model to learn tasks through a series of examples without explicit training. However,…
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…