Articles tagged with Artificial Intelligence
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
Two WPI projects harness AI to transform waste into energy and improve recycling. A digital twin simulates a complex chemical process, while an AI-powered robotic system identifies materials for recycling. These innovations aim to reduce municipal solid waste and support the circular economy.
A new quantum-secured data transmission architecture has been proposed to address the challenges of AI-driven data centers. The system achieves terabit-per-second capacity while defending against future quantum threats through self-homodyne coherent transmission and integrated quantum key distribution.
Researchers found that AI-generated content is a triple threat to Reddit moderators, posing concerns over decreasing content quality, disrupting social dynamics, and being difficult to govern. To address these issues, moderators are enacting rules and trying to preserve the community's humanity.
Researchers propose using the Agent Deed Consequence (ADC) model to program ethical values into smart city technologies. The model captures human moral judgments by considering agent intent, deed, and consequence, enabling AI systems to distinguish between legitimate and illegitimate orders.
The ESMO Guidance on the Use of Large Language Models in Clinical Practice (ELCAP) provides a structured set of recommendations for safely adopting AI language models in oncology. The guidance emphasizes the importance of human oversight, transparent tools, and continuously monitored systems to ensure trust in AI-driven cancer care.
Lehigh University researchers used machine learning to compare bone marrow extracted from the hip and shoulder, finding six proteins that distinguish between the two extraction sites. This study may lead to standardized BMAC extraction protocols and personalized treatments based on protein concentrations.
The European Society of Endocrinology has released the EndoCompass Research Roadmap, a major new initiative to align research efforts and improve funding strategies for hormone-related health challenges. The roadmap identifies specific research needs across eight endocrine specialties and five cross-cutting areas.
Researchers used five advanced LLMs to assess their logical reasoning capabilities and found that they consistently complied with requests for false medical information. Targeted training and fine-tuning improved LLMs' abilities to respond to illogical prompts accurately, but challenges remain in aligning models to every type of user.
Researchers highlight the importance of designing 'human-in-the-loop' systems with expert input for AI model training and validation. Machine learning models offer more interpretable results, but generative AI requires robust safeguards to protect patient data.
A recent study found that most users cannot identify AI bias in training data, unless it affects their own group. Researchers created 12 versions of a prototype AI system to detect facial expressions and tested how users might detect bias in different scenarios.
Recent research found that large language models are not yet able to consistently fool humans in conversations. They struggle with using discourse markers, opening and closing features, and subtle imitations. Despite rapid development, key differences between human and artificial conversations will likely remain.
Dr. Benjamin P. Brown proposes a targeted approach to improve the accuracy and speed of machine learning in drug discovery. His work focuses on creating a more generalizable deep learning framework for structure-based protein-ligand affinity ranking.
Researchers from MIT and the MIT-IBM Watson AI Lab have introduced a new training method that enables vision-language models to localize personalized objects in a scene. By using carefully prepared video-tracking data with contextual clues, the model is better able to identify the location of a specific object in a new image.
A new computer model called CogLinks simulates brain circuit decisions and adaptability, helping researchers understand how the brain misfires in psychiatric disorders. The model's results suggest that a specific pathway is essential for adaptability, and its predictions are confirmed by an fMRI study
Researchers built a digital archaeology framework to learn about ancient humans creating finger flutings, which are marks drawn on cave walls. The study used tactile and VR setups to explore AI image recognition methods, finding promising insights but also limitations.
A landmark study analyzed health data from over 600,000 patients across 10 countries to assess patient risk for non-ST-elevation acute coronary syndrome (NSTE-ACS). The AI-powered model GRACE 3.0 predicts risk more accurately and guides personalized treatment decisions.
VFF-Net applies label-wise noise labelling, cosine similarity-based contrastive loss, and layer grouping to improve image classification performance compared to conventional forward-forward networks. The algorithm reduces test errors on various datasets, enabling lighter and more brain-like training methods that make AI more sustainable.
A new large language model, LassoESM, has been developed to predict lasso peptide properties, enabling the acceleration of rational design for biomedical applications. The model was trained on thousands of lasso peptide sequences and demonstrated accurate prediction of various properties.
A new AI-based method optimizes the operation of solar power generation and battery storage systems, reducing imbalance penalties by approximately 47% compared to conventional control methods. The method maintains stable profits throughout the four seasons and can handle real-world uncertainties such as sudden weather changes and compl...
Researchers found that facial self-touching, particularly around the nose, chin, and cheeks, strongly correlates with stress levels during cognitive work. The study also suggests that this behavior may be an evolutionary, self-soothing mechanism to regulate stress.
Researchers at the University of Córdoba have developed two Artificial Intelligence methodologies capable of predicting extreme wind speeds with greater accuracy than traditional methods. The systems, trained on over 13 years of data, excel in forecasting severe events, allowing turbines to be shut down and preventing damage or collapse.
CompositesAI helps users create and analyze composite products without requiring in-depth technical knowledge. The platform is initially focused on rotor blades for air mobility, helicopters, and wind turbines, but its uses will expand to handle other composite structures.
Scientists from Japan developed a theoretical framework that explains how collective cells can perform complex tasks. The key is distributed information processing and reinforcement learning in the environment.
A new AI-powered tool, EZSpecificity, can predict the best enzyme-substrate combination for various applications. The tool outperformed existing models in accuracy, especially for halogenase enzymes.
A recent study reveals that the response to witnessing robot mistreatment depends on factors such as the robot's humanlike design and the observer's moral identity. Anthropomorphism influences empathy, encouraging customers to treat robots with dignity, while moral identity plays a crucial role in shaping behavioral contagion.
A novel AI optimization model called GAN-Solar has been developed to address the technical bottleneck of accurate short-term solar forecasting. The model achieves significant improvements on key metrics compared to existing advanced models, producing high-definition forecasts that capture crucial details.
Researchers developed a cyberattack detection system that uses federated learning and cloud coordination to detect DDoS attacks in 6G-ready smart grids without exposing user energy-use data. The system achieved high accuracy and precision, but trade-offs were observed in terms of resources consumption.
Researchers discovered a common relational structure across all participants when observing relationships between general activity patterns in neurons. This structure may be the brain's way of organizing information to ensure similar perceptions despite underlying neural coding differences.
CMU researchers use AI and robotic mobility to give everyday objects foresight, predicting interventions and moving across horizontal surfaces to help humans. The system uses computer vision and LLMs to reason about a person's goals, translating what the camera sees into a text-based description of the scene.
A new AI tool, SpectroGen, uses generative AI to quickly assess material quality by generating spectra in less than one minute. It can replace traditional methods that take several hours or days, improving productivity and efficiency in industries such as manufacturing and pharmaceuticals.
A new project aims to develop a computationally efficient model that accurately predicts how additive manufacturing process parameters influence the solidification microstructure of binary alloy solidification. This will enable optimization of additively manufactured parts with confidence in critical industries.
The project aims to improve undergraduate education by introducing situated case studies to help students understand AI complexity, critical reasoning, and applications. The initiative serves the national interest by preparing future professionals to use and develop AI, advancing their understanding of workforce innovation.
MetaSeg achieves the same segmentation performance as U-Nets but requires 90% fewer parameters, making medical image segmentation more cost-effective. The new approach leverages implicit neural representations to quickly adjust to new images and decode accurate labels.
A new AI system can generate realistic future X-rays alongside risk scores for osteoarthritis progression, giving doctors and patients a clear visual forecast of how the condition may develop over time. The system outperforms comparable tools in predicting osteoarthritis progression with nine times faster speed and accuracy.
A recent study used AI algorithms to analyze medical imaging and predict treatment response in patients with locally advanced nasopharyngeal carcinoma. The AI-based radiomics model achieved high accuracy in predicting treatment response and prognosis, revealing a link between imaging-derived features and the tumor microenvironment.
Researchers developed a more accurate cough-detection model using wearable health monitors' audio and movement data. The new model can distinguish between coughs and nonverbal sounds, improving the accuracy of respiratory disease tracking.
Researchers developed AI models to identify high-risk children for sepsis within 48 hours, enabling early preemptive care. The study used electronic health record data from the Emergency Department and showed robust balance in identifying at-risk children without overidentifying those who are not at risk.
Researchers at the University of Missouri have developed an AI-powered method to detect hidden hardware trojans in chip designs, offering a 97% accurate solution. The approach leverages large language models to scan for suspicious code and provides explanations for detected threats.
The ESMO Congress 2025 will cover key topics including precision oncology, antibody-drug conjugates in breast cancer, dose optimisation strategies, new treatment modalities for melanoma, and immunotherapy-based approaches across various cancer settings. Promising Phase-3 studies on ADCs in bladder and lung cancer are also expected.
The platform allows users to create personalized LLM-based chatbots, serving as a 24/7 teaching assistant or scraping campus websites to find needed resources. Illinois Chat has already been utilized in various departments and research groups, providing valuable feedback to make it more practical.
A new smartphone-based AI system accurately predicts avocado firmness and internal quality with high accuracy, enabling consumers to avoid overripe avocados. The technology has the potential to assess the ripeness and quality of other foods, reducing global food waste by 50% by 2030.
The report presents expert views on AI's potential benefits and risks in healthcare, including its application in clinical care, biomedical research, and business domains. The authors discuss the need for regulation, evaluation, and implementation of AI in healthcare to ensure its safe and effective use.
URNet transforms rapid event signals into accurate depth maps using local-global refinement and uncertainty-aware learning. The system produces confident predictions and adjusts response in low-confidence situations, enhancing robustness and trustworthiness in real-world conditions.
Research highlights AI scams targeting Latino population with deepfake, voice-cloning and AI voice bots. Technical writing courses can help increase AI literacy and detect deception.
Researchers at Kyushu University have developed a new method to build more energy-efficient magnetic random-access memory (MRAM) using thulium iron garnet. The team successfully produced thin films of platinum on the TmIG material, enabling high-speed and low-power information rewriting at room temperature.
Researchers from Shanghai Jiao Tong University and Hong Kong universities develop a comprehensive review on advanced AI chip design, focusing on energy efficiency, processing speed, and hardware scalability. The work provides a strategic roadmap for the development of high-performance AI chips through interdisciplinary collaboration.
Geoffroy Hautier, a materials scientist at Rice University, has been elected a fellow of the American Physical Society for his groundbreaking research in high-throughput computational materials design and discovery. His work bridges quantum mechanics, computation, and artificial intelligence to accelerate the discovery of new materials...
Researchers tested an AI-supported diagnostic method at rural hospitals in Kenya and Tanzania, analyzing cervical cell samples and human papilloma virus (HPV) samples using digital tools. The study found that the AI method worked well technically but faced challenges with inconsistent images and follow-up care.
Researchers developed an AI-based generative approach to discovering technology opportunities from patent maps using machine learning. The system translates patent vacancies into human-readable text, enabling the identification of untapped technologies and facilitating innovation forecasting.
Plant functional diversity varies greatly depending on seasonal cycles and wet-dry periods, according to a new study that analyzed 4,000 satellite images. The researchers used AI algorithms to derive plant traits from the images and calculated quantitative measures of functional diversity.
Researchers at Harvard Medical School developed Dr. CaBot, an AI system that can generate a differential diagnosis and explain its nuanced reasoning like an expert diagnostician. The system reached a comparable final diagnosis to a human expert in a published case presentation, demonstrating its potential for medical educators and phys...
A new approach called Streamlined Life Cycle Assessment (SLCA) uses AI, 3D modeling, and secondary databases to assess a product's environmental footprint. SLCA cuts the time required for assessments from months to just one week, while maintaining accuracy.
Researchers at Texas A&M University have developed a new framework to protect industrial processing facilities from cyber threats. The framework identifies vulnerabilities, detects abnormal activity in real-time, and provides safeguards and mitigation strategies to maintain safe operations.
Researchers developed DeMemSeg, an AI-driven pipeline that accurately segments overlapping membrane structures with accuracy comparable to expert manual analysis. The approach enables large-scale, objective, and quantitative analysis of morphological data, providing a foundational technology for advancing disease mechanisms.
Researchers at the University of Tokyo developed an AI-based framework, scHDeepInsight, to rapidly and consistently identify immune cells from gene data. The system uses hierarchical learning and image-based representation to distinguish broad and fine subtypes with improved accuracy and consistency.
A council of five AI models trained to deliberate together achieved higher accuracy on medical licensing exams than any single chatbot alone. This collaborative approach showed promise for improving the reliability and trustworthiness of AI tools in healthcare.
The Indo-Swiss Workshop on 'Research Integrity in the Age of AI' brought together policymakers, scientists, and publishers to explore responsible AI use. Frontiers shared its pioneering approach to detecting research fraud using human-in-the-loop AI systems.
Researchers at NC State University identified a hardware vulnerability that allows attackers to compromise AI privacy by exploiting ML accelerator behavior. The GATEBLEED vulnerability exploits power gating in AI accelerators, creating an observable timing channel for attackers.
A new study uses general-purpose AI to classify real cosmic events and explain its reasoning without complex training. The model achieved approximately 93% accuracy and provided plain-English explanations for every classification.
Researchers are developing sophisticated formulas to enable AI to interpret massive biological datasets and uncover disease insights. Dr. Xinlei Wang's team is creating AI models that can analyze complex biomedical data using a Bayesian framework, producing clear and interpretable results.