Data Science is a multidisciplinary field that involves extracting knowledge and insights from data using scientific methods, processes, algorithms, and tools. It combines aspects of statistics, mathematics, computer science, and domain expertise to analyze and interpret data, uncover patterns, make predictions, and solve complex problems. Data scientists utilize techniques such as data mining, machine learning, statistical modeling, and data visualization to extract valuable information and support data-driven decision-making in various industries and domains.
The European Centre for Medium-Range Weather Forecasts (ECMWF) has launched the Artificial Intelligence Forecasting System (AIFS), a cutting-edge AI-driven model that improves weather prediction accuracy by up to 20% while reducing energy consumption by 1,000 times compared to traditional methods.
Researchers developed an AI system that improves tracking of urban green spaces using satellite imagery, significantly enhancing accuracy and revealing disparities in vegetation distribution.
Researchers at the University of Navarra have developed a novel AI framework that enhances fairness and reliability in machine learning models used for critical decision-making, reducing biases linked to race, gender, and socioeconomic status.
San Francisco State University researchers created a step-by-step machine-learning tutorial to predict antibiotic resistance, making complex concepts accessible to beginners in biology and health sciences.
Scientists at the University of California, Riverside, have developed an unsupervised machine learning tool that identifies patterns in LIGO's environmental data, reducing noise and improving gravitational wave detection.
Researchers have developed ProtET, a cutting-edge AI model that uses multi-modal learning to enable precise, text-guided protein editing, paving the way for breakthroughs in biotechnology and medicine.
DeepSeek has developed an agile AI model that outperforms larger systems despite using less advanced hardware, reshaping global AI competition and policy discussions.
Researchers at West Virginia University are developing an interdisciplinary curriculum to equip humanities faculty with the tools to explore AI's ethical, social, and technical dimensions and integrate AI into their courses.
Research introduces an explainable AI model that predicts ICU length of stay with 90% accuracy while providing evidence-based insights for informed decision-making. This innovative approach aims to optimize resource allocation, reduce overcrowding, and improve patient outcomes.
Research explores how Natural Language Processing (NLP) models like ChatGPT revolutionize understanding and generating human language. It delves into their mechanics, training processes, potential applications, and ethical considerations in AI's rapid evolution.
A new mathematical model based on Bayesian statistics offers unprecedented insights into the scalability of AI identification techniques, balancing technological benefits with privacy risks.
Researchers developed an AI-powered platform, TrialTranslator, to assess the real-world applicability of cancer clinical trials, helping identify which patients will benefit from treatments.
A €3 million EU-funded research project, FORSEE, led by University College Dublin, will explore societal impacts and benefits of AI while broadening the definition of AI "success" to include sustainability, gender equity, and societal engagement. The project aims to inform EU regulatory frameworks and empower stakeholders.
Large language models like GPT-4 excel in medical exams but falter in realistic doctor-patient conversations, prompting the creation of the CRAFT-MD framework to better evaluate their real-world clinical capabilities.
OpenAI's MLE-bench evaluates AI agents on machine learning engineering tasks using Kaggle competitions, revealing promising performance in nearly 17% of trials. The benchmark is open-sourced to boost research on autonomous ML engineering.
Researchers from the University of Granada examine AI's history, capabilities, emotional simulation, risks, and the concept of singularity, urging ethical oversight and transparency in future AI developments.
Researchers introduced Requirement-Oriented Prompt Engineering (ROPE), a new training method that significantly improves novices' ability to write clear, effective requirements for LLMs, leading to better task delegation and LLM outputs.
This study presents a robust data-driven framework for identifying conservation laws in systems without known dynamics. By leveraging stable singular vectors, the method accurately reconstructs conservation laws with minimal data, proving versatile across various scientific applications beyond biology.
Dartmouth researchers develop MoodCapture, an AI-powered smartphone app that detects early symptoms of depression with 75% accuracy using facial-image processing, promising a new tool for mental health monitoring.
This study harnesses the CatBoost algorithm to predict transition temperatures (Tc) of superconducting materials, addressing challenges in dataset refinement and feature selection. Leveraging the Jabir and Soraya packages for generating atomic descriptors and selecting crucial features, the model achieved high accuracy with an R-squared (R2) of 0.952 and root mean square error (RMSE) of 6.45 K. Additionally, a novel web application for Tc prediction underscores the impactful synergy between AI and materials science.
Terms
While we only use edited and approved content for Azthena
answers, it may on occasions provide incorrect responses.
Please confirm any data provided with the related suppliers or
authors. We do not provide medical advice, if you search for
medical information you must always consult a medical
professional before acting on any information provided.
Your questions, but not your email details will be shared with
OpenAI and retained for 30 days in accordance with their
privacy principles.
Please do not ask questions that use sensitive or confidential
information.
Read the full Terms & Conditions.