In recent years, advances in artificial intelligence (AI) and machine learning (ML) have greatly enhanced untargeted metabolomics, a field which allows for an unbiased global analysis of metabolites in the body and can yield crucial insights into human health and disease. Through high-resolution mass spectrometry, untargeted metabolomics identifies key metabolites and chemicals that may contribute…
Metabolomics uses a high-throughput technique to analyze various metabolites and small molecules in biological samples to provide important insights into human health and disease. Untargeted metabolomics is one application that enables a comprehensive analysis of the metabolome, identifying crucial metabolites that indicate or contribute to health conditions. The advent of artificial intelligence (AI) and machine…
For engineers, on-call shifts can be challenging, as they often need to identify and fix system issues promptly. This typically involves analyzing vast amounts of data and logs, which is time-consuming and can be even more daunting, especially during after-hours. Finding the root cause of a problem is a critical step in the process, although…
On-call shifts pose significant challenges for engineers. When system issues occur, it is typically the on-call engineer's responsibility to diagnose and remedy the problem rapidly. This often involves poring over various data logs, a process that can be both time-consuming and mentally taxing, particularly outside of regular working hours.
A range of tools currently exist to…
Enhancing product security remains a major challenge for businesses, given the frequency of false positives from conventional Static Application Security Testing (SAST) technologies and the complexities of addressing the identified vulnerabilities. However, a breakthrough GitHub application called ZeroPath promises a solution by automating the detection, verification and resolution of security vulnerabilities in code.
ZeroPath is designed…
The traditional model of running large-scale Artificial Intelligence applications typically relies on powerful yet expensive hardware. This creates a barrier to entry for individuals and smaller organizations who often struggle to afford high-end GPU's to run extensive parameter models. The democratization and accessibility of advanced AI technologies also suffer as a result.
Several possible solutions are…
Large Language Models (LLMs) have transformed natural language processing, despite limitations such as temporal knowledge constraints, struggles with complex mathematics, and propensity for producing incorrect information. The integration of LLMs with external data and applications presents a promising solution to these challenges, improving accuracy, relevance, and computational abilities.
Transformers, a pivotal development in natural language…
Language models are widely used in artificial intelligence (AI), but evaluating their true capabilities continues to pose a considerable challenge, particularly in the context of real-world tasks. Standard evaluation methods rely on synthetic benchmarks - simplified and predictable tasks that don't adequately represent the complexity of day-to-day challenges. They often involve AI-generated queries and use…
Scikit-fingerprints, a Python package designed by researchers from AGH University of Krakow for computing molecular fingerprints, has integrated with computational chemistry and machine learning application. It specifically bridges the gap between the fields of computational chemistry that traditionally use Java or C++, and machine learning applications popularly paired with Python.
Molecular graphs are representations of…
Authorship Verification (AV), a method used in natural language processing (NLP) to determine if two texts share the same author, is key in forensics, literature, and digital security. Originally, AV was primarily reliant on stylometric analysis, using features like word and sentence lengths and function word frequencies to distinguish between authors. However, with the introduction…
SciPhi has recently launched Triplex, a cutting-edge language model specifically designed for the construction of knowledge graphs. This open-source innovation has the potential to redefine the manner in which large volumes of unstructured data are transformed into structured formats, significantly reducing the associated expenses and complexity. This tool would be a valuable asset for data…
SciPhi has introduced a cutting-edge language model (LLM) named Triplex, designed for constructing knowledge graphs. This open-source tool is set to transform the way large sets of unstructured data are turned into structured formats, all while minimizing the associated cost and complexity. The model is available on platforms such as HuggingFace and Ollama, serving as…