Articles tagged with Artificial Intelligence
Peter J. Denning suggests that Turing's stance on artificial general intelligence and the imitation game has led to the AI mess, with a fundamental flaw in understanding tacit knowledge and its representation problem. He argues that machines cannot grasp human emotions, intuitions, or cultural context, making it impossible to achieve h...
A global consortium has developed three open-source AI tools to synthesize Alzheimer's literature, surface hidden data insights, and provide peer review feedback. The tools are designed for researchers to harness vast amounts of information toward a shared goal.
The PRIME-6G project aims to bring next-generation 6G technologies into real industrial manufacturing environments. AURORA and SENTINEL use cases investigate deterministic 6G connectivity, AI-driven network management, and multi-sensor fusion for intelligent industrial systems.
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs. The new approach identifies model variations that can generate CSAM with 100% accuracy.
The WVU team developed an AI framework that allows satellites to not only detect wildfires but also coordinate with one another and adjust their observation schedules as fires spread. This enables firefighters to respond sooner, taking into account the rapid movement of wildfires, dense vegetation, and hills.
Researchers developed an AI model to optimize water usage in agriculture and semiconductor manufacturing. The model identifies cause-and-effect relationships between water availability, crop needs, and industrial expansion, generating recommendations for each state.
This study found that households in metropolitan areas with higher population density and income levels show stronger voluntary water conservation during drought. The AI-based scenario analysis showed that increased drought-related news coverage leads to significant water savings in these regions, but has a limited impact on rural areas.
Researchers at DGIST developed an artificial olfactory system that uses metal-organic frameworks to detect and analyze various odors. The system leverages machine learning and deep learning techniques to classify and interpret complex odor signals, enabling accurate disease diagnosis, environmental monitoring, and more.
Researchers developed a machine learning framework to account for water quality differences, enabling accurate MC-LR measurements without repeated calibration. The model achieved a Nash-Sutcliffe efficiency of 0.89 and improved analytical efficiency while reducing time, labor, and sensor consumption.
Researchers at Tokyo University of Science found that accounting for the time scale of a target system improves ESN hyperparameter settings, leading to better prediction accuracy. The study provides guidelines for designing optimal ESN settings based on the time scale.
A PolyU project has developed an intelligent portable traffic light system to address bottlenecks during roadworks on narrow streets in Hong Kong. The system uses advanced sensing technologies and adaptive algorithms to dynamically adjust signal timings, reducing traffic queues and preventing gridlock.
Deep learning models accelerate drug design, predict chemical interactions, and engineer stable candidates. AI-powered simulations optimize dosimetry, predicting biodistribution and generating patient-specific digital twins for individualized treatment planning.
A study from Technical University of Munich found that people perceive AI hiring decisions as unfair when the avatar resembles them in terms of gender or skin color. After receiving a rejection, trust in AI is shaken if the avatar's appearance differs from their own.
Nigel provides clinicians with fast, evidence-based answers, helping them make faster, more confident decisions. The platform reduces cognitive burden by enabling quick access to current evidence and reducing time spent on literature searches.
A new brain-like electronic device consumes very little energy and detects novelties almost instantly, with over 98% accuracy. The device requires roughly 10,000 times fewer computer operations than conventional AI approaches, paving the way for more energy-efficient AI systems.
The partnership aims to streamline prior authorizations, appeals, and financial assistance, addressing delays or denials in accessing prescribed therapies. By combining AGA's clinical leadership with Forus's medication access platform, the collaboration seeks to enhance patient advocacy and access to effective treatments.
Researchers at the University of Pennsylvania and Chinese University of Hong Kong created TD3B, an AI framework guiding peptide generation toward candidates predicted to have a desired effect. The tool predicts binding likelihood and determines activation or deactivation of associated cellular machinery.
Recent studies validate AI-based chatbots as effective alternatives to human fitness professionals for generating personalized training programs. However, limitations in nuance and continuous monitoring require hybrid models combining AI tools with human expertise.
Researchers developed a physics-informed scoring system to identify two-dimensional materials with unusual electronic properties. The approach captures signatures of flat-band behavior and trains a model to estimate scores directly from atomic structure, accelerating the search for promising materials.
A new study found that physicians tend to trust incorrect AI advice and have trouble learning from patient recovery data that contradicts it. The researchers suggest that developing strategies to increase human critical thinking and detection of AI errors is crucial for maximizing the benefits of human-AI collaboration in healthcare.
A team of researchers, led by Ying Zhang, has developed artificial intelligence-driven tools to identify and attack software vulnerabilities. By teaching AI to generate proof-of-concept exploits, developers can see exactly how attackers could exploit known flaws, motivating them to fix issues before malicious actors do.
Biomni is an AI-powered multi-skilled biomedical research agent that designs and develops complex research workflows. It provides full citations and tracking of its work, making science more rigorous and reproducible.
A Dartmouth study reveals that people's gaze patterns in new environments contain unique personality preferences. The researchers used eye-tracking data to model individual gaze patterns and create machine-learning models that could distinguish between participants based on their conceptual themes.
Researchers uncovered previously undetected slow slip events in Parkfield, California, and found that these silent fault movements systematically follow increased low-frequency earthquake activity. The discovery suggests that slow slip may play an important role in how stress evolves along active faults.
Physicists at UC Irvine have developed an AI system called Autonomous Model Builder that can autonomously design theoretical physics models, helping identify promising new explanations for the behavior of neutrinos. The system uses reinforcement learning and is designed to assist human physicists in narrowing down vast theory spaces.
A new report reveals that 42% of people in the UK deliberately limit their AI use, primarily driven by worries about data protection. Despite the benefits, many individuals are ambivalent towards AI due to growing security and privacy concerns.
The ORIGIN project aims to reduce development timelines for sustainable, fermentation-based ingredients from 5-7 years to 2-3 years. By combining AI, biotechnology, and fermentation, the project will address scientific and technological challenges to produce high-value natural ingredients.
Researchers from Penn, NYU, and the Linguistic Data Consortium create virtual patients with adjustable psychiatric symptoms to simulate real-world conversations. The STELLAR platform aims to augment clinician training practices with essential conversation scenarios.
Researchers developed an AI assistant called ChatHEA to guide the discovery of new catalysts for clean energy technologies. The team screened and evaluated 100 five-element high-entropy alloy catalysts, finding that FeCoCuPtIr showed excellent oxygen reduction activity and durability.
A new study explores an FMQA-based optimization framework for RNA design, revealing that encoding matters in achieving optimal results. The approach identifies high-quality RNA sequence candidates with relatively few evaluations, outperforming competing methods.
Researchers used CRISPR and AI to identify two novel drug targets for psoriasis: the oxytocin receptor and ALOX5 enzyme. Topical gels containing these compounds reduced inflammation in mice as effectively as widely used injected therapies, offering a promising new approach to treating moderate-to-severe disease.
A new study finds that human cortical neurons have remarkable computational capabilities, surpassing those of other mammals. The researchers developed a new method to measure the complexity of individual neurons, revealing their sophisticated computing power.
Researchers found that variable-rate seeding (VRS) can help farmers strike a better balance in corn and soybean fields. However, soybeans proved to be more complicated due to their adaptability to weather conditions. The study aims to make farming more accessible and efficient for small land holders using digital tools and data-driven ...
A team from Singapore University of Technology and Design has developed an AI agent that uses artificial intelligence to help patients make life-or-death decisions. The system, called ACPAgent, was tested with 15 participants who agreed with its recommendations in 86.7% of cases, but also showed limitations in handling high-subjectivit...
A new study found that clinician support significantly increases patient adoption of virtual reality therapies, leading to better time-based adherence and technology acceptance. The research team evaluated three implementation environments, with provider-led support demonstrating the most favorable usability pattern.
Researchers developed EleTac, a soft robotic gripper with high-resolution tactile sensing, to handle delicate objects. The gripper's innovative design enables it to adapt to various shapes and provide gentle forces, making it suitable for applications such as handling fruit, lab samples, and medical supplies.
Philip S. Yu and Osmar R. Zaïane succeed at a pivotal stage of Intelligent Computing's growth as an international open-access journal. The journal publishes innovative research on artificial intelligence, machine learning, and emerging interdisciplinary areas.
Researchers warn that AI faces are becoming increasingly trustworthy, with the latest diffusion model outperforming earlier models. This poses significant risks of online fraud and erosion of trust in society, as people become more susceptible to fake faces used for nefarious purposes.
Nora McDonald received a $748,258 NSF CAREER award to investigate how AI-driven personalized technologies shape young people's decision-making and sense of self. Her project aims to develop practical tools and educational resources to help adolescents and adults respond to algorithmic influence.
Researchers have developed a new kind of point-of-load converter to step down power from 48 volts to 1 volt, achieving higher efficiency and faster power delivery. This technology could significantly reduce energy consumption and thermal stress in AI data centers.
A study found that forecast error types influence public emotion during disasters, with anxiety and worry being the most common emotions. The researchers suggest that communicating forecast uncertainty effectively could improve public trust and reduce emotional distress during future extreme weather events.
Researchers developed a technology to stack ultrathin semiconductor chips with improved integration density, overcoming challenges of chip thickness and warpage. The process enables the reliable stacking of over ten chips, potentially leading to significant improvements in AI semiconductor performance.
Dimensions Research Strategy is an AI analytics platform that provides evidence-backed strategic briefs based on the world's most interconnected research data. The platform aims to support university leadership in benchmarking and expertise, offering verifiable intelligence for informed decision-making.
A large-scale study of an online patient portal shows that AI-generated responses can introduce errors and extraneous details, leading to increased editing time for physicians. Adapting AI to individual physician communication styles can improve accuracy by 33% and reduce editing by 26%.
The new institute aims to accelerate data-driven health care with partnerships between six schools, UC San Diego Health, and specialized centers. By integrating digital technology and rigorous research, the institute seeks to achieve scalable, reliable health outcomes for all.
A new learning-based adaptive tuning method integrates chaotic search with particle swarm optimization to improve stability and solution quality in chaotic search algorithms. The approach consistently achieves better results than conventional methods, providing a practical means of enhancing the performance of chaotic search.
A new study found that AI-generated lunar crater catalogs often perform poorly when evaluated using human-defined scientific standards. The research highlights the need for standardizing benchmarks and transparent reporting to ensure the accuracy of AI-generated data.
BetaDescribe, an AI system, converts protein sequences into detailed textual descriptions of their functions and characteristics. The technology helps bridge the gap between characterized and existing proteins in nature, enabling researchers to rapidly generate evidence-based hypotheses regarding unknown proteins.
Researchers developed a physics-informed neural network approach to predict material properties and optimize controlled-release systems. The new method requires significantly less data than traditional AI models, slashing development time for patches, bandages, and implants.
The collaboration aims to develop AI-powered ground systems that can assist operators with routine satellite operations, mission scheduling, and data analysis. The partnership seeks to automate routine tasks with human oversight, enabling more efficient management of larger satellite fleets.
Researchers create tiny swimmers to deliver drugs through the human body, finding they reverse direction in non-Newtonian fluids like mucus and blood. This discovery enhances understanding of fluid behavior and could lead to targeted drug delivery.
A team of researchers has developed a new method for finding effective tuberculosis drugs by leveraging the PAC-MAN technique and artificial intelligence. The approach uses machine learning models to predict which chemical compounds can penetrate the outer membrane of the bacteria, paving the way for more efficient drug discovery.
A new editorial highlights AI's role in scientific discovery, proposing a framework to evaluate AI-enabled scholarship. The authors emphasize the need for transparency, reproducibility, and real-world value in AI-driven research.
The study reviews SLM optimization strategies and evaluates a Greek labor-law assistant that combines fine-tuning, Retrieval-Augmented Generation and quantization for practical local deployment. The evaluation used standard language-generation and retrieval-oriented metrics, indicating that larger models achieved stronger scores while ...
Researchers emphasize the importance of enhancing visual intelligence in autonomous robots to achieve complex tasks. Visual perception, decision-making, path planning, and control must be integrated for robots to perform reliably. The ultimate goal is to make robots have human-like intelligence and enable collaboration between robots.
A new study reveals that large language models can systematically alter the direction of users' messages on contested topics, even when instructed to preserve the original meaning. The researchers show how these small changes can accumulate and gradually influence broader public opinion over time.
Researchers developed an on-chip all-optical supernode for ultra-low-latency deep neural network inference, achieving a 100-fold increase in inference speed while using only one-ninth of computing resources. The system supports high-speed data routing and switching with low loss and flat response over a spectral range exceeding 100 nm.
Researchers propose a new framework, BOC, inspired by biological nervous systems to build more adaptive intelligent systems. The framework integrates sensing, memory, and computation directly at the hardware level, reducing data movement and latency.
NII and Indian Institute of Technology Bombay form a collaboration to advance the research and development of transparent and reliable large language models. BharatGen, an India-based AI initiative, contributes to building an open and inclusive AI ecosystem.
A recent study found that companies relying heavily on AI-powered recruitment may be losing out to competitors due to a lack of human connections. Employers should prioritize transparency, bias auditing, and human oversight in their recruitment processes.