AI is used in data analysis to extract insights, discover patterns, and make predictions from large and complex datasets. Machine learning algorithms and statistical techniques enable automated data processing, anomaly detection, and advanced analytics, facilitating data-driven decision-making in various industries and domains.
A new research chapter argues that artificial intelligence could help social entrepreneurs overcome persistent funding barriers by improving how investors and lenders assess financial risk, social impact, and business potential. The authors suggest that AI-driven fintech tools, impact data, and alternative funding models may create more inclusive and transparent pathways to finance for mission-led enterprises.
Researchers at Rice University brought together international experts to explore how artificial intelligence and machine learning could help the Deep Underground Neutrino Experiment analyze vast datasets, detect rare signals, and improve detector operations. The workshop highlighted how AI could accelerate neutrino research by strengthening simulation, monitoring, and data management across one of the world’s most ambitious physics collaborations.
Artificial intelligence is reshaping environmental science by integrating large, complex datasets to enable predictive, system-level insights across water, soil, air, and waste systems. It is evolving from a supporting analytical tool into an active partner in scientific discovery, enabling real-time monitoring and more precise environmental management.
Solid-state batteries promise safer, higher-energy storage, but progress is limited by the difficulty of designing solid electrolytes that balance conductivity, stability, and interfacial performance.
This review shows how AI agents are transforming materials discovery by coordinating data analysis, modeling, simulation, and experimental planning to accelerate solid electrolyte development.
Researchers at Chiba University have developed the Topology-Aware Multiscale Feature Fusion (TA-MFF) network, an advanced deep learning model that enhances the decoding of motor imagery EEG signals for brain-computer interface (BCI) applications. By integrating spectral, topological, and spatiotemporal features, the system achieves superior accuracy in interpreting neural activity linked to imagined movement.
Researchers at Chung-Ang University in South Korea have developed DiffectNet, an AI-powered diffusion model that reconstructs hidden internal defects in structures with high precision, overcoming the physical limits of traditional non-destructive testing. The breakthrough enables real-time defect imaging for critical sectors such as energy, aerospace, and semiconductors.
A new comprehensive survey addresses the critical gap between rapid advances in image generation models and the lagging development of evaluation methods, covering both human and automatic assessment across 10 image generation tasks. It proposes a unified protocol with six core evaluation dimensions, offering the first in-depth analysis of human evaluation and systematically reviewing emerging automatic metrics and benchmarks.
Researchers at Osaka Metropolitan University developed an AI-assisted infrared thermography system to non-invasively monitor body temperature in calves by automatically identifying eye and nose regions. This method captured consistent temperature patterns, offering a reliable, contact-free approach for assessing animal health and stress.
ThinkCyte has launched MorphoScan Cloud, an AI-powered platform that centralizes, accelerates, and deepens cell analysis for VisionSort users by leveraging secure cloud access and advanced analytics.
The platform enables global research teams to share, process, and interpret complex cellular data with unprecedented speed and precision.
AI-driven models are transforming wine evaluation by matching expert ratings, generating human-like reviews, and offering personalised taste profiling, while also improving transparency in decision-making. This body of work highlights how AI can augment—but not replace—the sensory and cultural dimensions of wine appreciation.
A University of Georgia study tested how large language models like Mixtral grade middle school science responses. While fast, these AIs often use keyword shortcuts that undermine accuracy without human-designed rubrics.
Mount Sinai is the first U.S. medical school to provide all students and selected faculty with access to OpenAI’s secure ChatGPT Edu platform. The initiative aims to integrate responsible, advanced AI into education, research, and clinical reasoning while safeguarding data and promoting ethical use.
More than 1,600 scientists across nine U.S. national labs took part in a first-of-its-kind AI Jam Session to test OpenAI’s cutting-edge models on complex scientific tasks. Brookhaven researchers explored AI’s potential in spectroscopy, accelerator physics, nanomaterials, and more.
A new neural network called DINGO-BNS analyzes gravitational waves from neutron star mergers thousands of times faster and more accurately than traditional methods, enabling near-instant alerts for telescopes to capture critical signals. This breakthrough could revolutionize multi-messenger astronomy and our understanding of extreme physics.
Researchers from KIIT and Chandragupt Institute of Management explore how machine learning transforms big data challenges into opportunities, enabling industries to harness vast data resources effectively.
Businesses in the UK are shifting towards skill-based hiring in AI roles, with specific technical skills now outweighing traditional degrees in many cases. AI expertise commands higher salaries, with industry-specific know-how offering wage premiums surpassing those for formal education.
Researchers highlight how AI and big data are transforming crop breeding, ushering in "Breeding 4.0" with intelligent, data-driven precision to enhance global food security.
LLM4SD is a cutting-edge AI tool that can analyze scientific literature, generate hypotheses, and predict molecular properties with high accuracy, making scientific discovery faster and more transparent. The open-source system also provides explainable insights, unlike traditional black-box validation tools.
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 highlight the limitations of classical networks in capturing complex multi-agent interactions, proposing higher-order structures like simplicial complexes and hypergraphs to model real-world systems more effectively.
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.