AI and ML in Untargeted Metabolomics and Exposomics: Metabolomics employs a high-throughput approach to measure a variety of metabolites and small molecules in biological samples, providing crucial insights into human health and disease. One application, untargeted metabolomics, allows for an unbiased global analysis of the metabolome, identifying key metabolites that contribute to or indicate health…
A major development in artificial intelligence, multimodal large language models (MLLMs) combine verbal and visual comprehension to produce more accurate representations of multimodal inputs. Through the integration of data from multiple sources, including text and images, these models improve understanding of intricate relationships between various modalities. Because of this integration, sophisticated tasks requiring a thorough…
The recent development of large language models (LLMs) has transformed the field of Natural Language Processing (NLP). LLMs show human-level performance in many professional and academic fields, showing a great understanding of language rules and patterns. However, they often struggle with reasoning reliably and flexibly. This problem likely comes from the way transformers, the underlying…
The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has highlighted the critical need for large, diverse, and high-quality datasets to train and evaluate foundation models. However, acquiring such datasets presents significant challenges, including data scarcity, privacy concerns, and high data collection and annotation costs. Artificial (synthetic) data has emerged as a promising…
In the field of Artificial Intelligence, open, generative models stand out as a cornerstone for progress. These models are vital for advancing research and fostering creativity by allowing fine-tuning and serving as benchmarks for new innovations. However, a significant challenge persists as many state-of-the-art text-to-audio models remain proprietary, limiting their accessibility for researchers. Recently, a…
Multi-modal generative models integrate various data types, such as text, images, and videos, expanding AI applications across different fields. However, optimizing these models presents complex challenges related to data processing and model training. The need for cohesive strategies to refine both data and models is crucial for achieving superior AI performance. A major issue in…
SciPhi has recently announced the release of Triplex, a state-of-the-art language model (LLM) designed specifically for knowledge graph construction. This open-source innovation is poised to revolutionize how large quantities of unstructured data are converted into structured formats, significantly reducing the cost and complexity traditionally associated with this process. Available on platforms like HuggingFace and Ollama,…
Authorship Verification (AV) is critical in natural language processing (NLP), determining whether two texts share the same authorship. This task holds immense importance across various domains, such as forensics, literature, and digital security. The traditional approach to AV relied heavily on stylometric analysis, which uses linguistic and stylistic features like word and sentence lengths and…
In computational chemistry, molecules are often represented as molecular graphs, which must be converted into multidimensional vectors for processing, particularly in machine learning applications. This is achieved using molecular fingerprint feature extraction algorithms that encode molecular structures as vectors. These fingerprints are crucial for tasks in chemoinformatics, such as chemical space diversity, clustering, virtual screening,…
The paper addresses the significant challenge of evaluating the tool-use capabilities of large language models (LLMs) in real-world scenarios. Existing benchmarks often fail to effectively measure these capabilities because they rely on AI-generated queries, single-step tasks, dummy tools, and text-only interactions, which do not accurately represent the complexities and requirements of real-world problem-solving. Current methodologies…