Articles tagged with Artificial Intelligence
Researchers created an AI algorithm that can identify obstructive sleep apnea (OSA) using ECG test results, making it faster and cheaper to spot the condition. The innovation reveals a higher visibility of OSA on ECG in females compared to males, suggesting they may suffer more cardiovascular damage.
The FAU College of Engineering and Computer Science has established the 'Ubicquia Innovation Center for Intelligent Infrastructure' to develop transformative technologies. The center will empower students and faculty to create AI-First solutions for a smarter, more connected world.
A Michigan State University-led study examines how well AI personas can detect human deception and compares their performance to humans. The results show that AI is more lie-biased and less accurate than humans, highlighting the need for improvement before generative AI can be used for deception detection.
Researchers found that non-expert users' intuitive prompts triggered biased responses as effectively as expert 'jailbreak' techniques. The study used a competition to identify biases in generative AI models and suggests strategies for mitigating issues.
A team of researchers proposes a theoretical framework to induce non-reciprocal interactions in solid-state systems using light. By applying dissipation-engineering scheme, they predict a non-equilibrium phase transition called non-reciprocal phase transition, leading to a spontaneous and continuous rotation of magnetization.
Researchers discover that super-recognisers don't just process more information, but instead focus on the most informative parts of a face to aid in recognition. This skill cannot be learned and is an automatic, dynamic way of picking up unique facial features.
A study found that undergraduate computer science students initially increase their trust in generative AI programming tools, but long-term trust remains unclear. To address this, researchers recommend that educators provide tasks-based training and ensure students comprehend and modify code without relying on AI assistants.
A UT Dallas team developed an electrochemical biosensor that accurately identifies eight volatile organic compounds linked to thoracic cancers. AI analysis enhances the accuracy of breath samples, showing promise for early lung cancer detection and improved patient outcomes.
A new empirical study by computer scientists reveals that software developers tend to scrutinize AI-generated code less critically and learn less from it. In contrast, human pairs are more likely to engage in broader discussions and critically examine each other's contributions. The study highlights the limitations of AI assistants in ...
The report proposes initiatives to reduce firearm violence, including using AI and addressing social causes. It also highlights the need for primordial prevention, which involves changing inequitable social structures to prevent violence.
The Center for Computational and AI-enabled Imaging Sciences brings together experts to develop AI-powered medical imaging applications that integrate information from different imaging types. This may include identifying previously unknown early indicators of disease onset.
The new tool, called FSNet, combines machine learning and optimization to find feasible solutions quickly while ensuring constraints are met. It can unravel complex problems several times faster than traditional solvers and even outperform pure machine learning approaches.
A small-scale neuromorphic computer prototype learned patterns and made predictions using fewer training computations than conventional AI systems. The prototype, developed by the University of Texas at Dallas team, integrates memory storage with processing, allowing for more efficient AI operations with lower costs.
The University of Copenhagen has launched a new AI Package to strengthen its research in artificial intelligence and make it more accessible to society. The package aims to bring together various initiatives and research centers across the university to develop AI in a responsible, sustainable, and democratic way.
The SCALE taxonomy introduces a novel framework of educational objectives that align human learning with the emerging needs of the AI age. It encompasses scientific literacy, computational thinking, AI literacy, metacognition, and engineering and design thinking.
Researchers warn that advances in AI and neurotechnology are outpacing our understanding of consciousness, with potential serious ethical consequences. A better understanding of consciousness could have major implications for AI, prenatal policy, animal welfare, medicine, mental health, law, and emerging neurotechnologies.
Researchers from Chiba University developed an AI-based spatial framework to identify areas with limited vertical greening, revealing uneven distribution and high-priority zones for improvement. The framework provides a citywide map of vertical greenery, aiming to enhance cooling, biodiversity, and urban resilience.
Researchers at the University of Surrey have developed a brain-inspired approach to improve artificial neural networks' performance without sacrificing accuracy. The method, called Topographical Sparse Mapping (TSM), rethinks how AI systems are wired by connecting each neuron only to nearby or related ones.
Researchers at UMass Amherst have developed an app that uses Apple Watch data to monitor sleep stages and predict Alzheimer's disease risk. The app, BIDSleep, collects heart rate data and feeds it into an AI model that accurately identifies sleep stages 71% of the time.
A new study from researchers at Dell Medical School found that AI-powered tools can improve patient decision-making and outcomes for knee replacement surgery. Patients who used the digital twin tool reported higher decision quality, less regret, and better knee function compared to those who received educational materials alone.
Researchers have developed an AI-driven facial recognition system called AcroFace that can detect acromegaly by analyzing facial photographs. The system achieved a success rate of 93% in a preliminary trial, outperforming previous attempts. Early detection could lead to timely treatment and improved outcomes for patients with this rare...
Researchers developed an AI-driven framework to predict which plant species are most likely to become invasive before they even arrive in a new location. The methodology combines decades of ecological data with machine learning methods to identify high-risk species.
A new collaboration between Rice University and Baylor College of Medicine aims to predict kidney injury sooner after heart surgery, potentially reducing hospital stays and mortality. The project uses artificial intelligence to analyze vast amounts of data from thousands of cardiac surgery patients.
Researchers studied beaver ponds' effect on groundwater recharge, finding a 10-fold increase in recharging water. They also explored evapotranspiration and found it can reduce recharge in certain soil structures. The study highlights the need for comprehensive analysis of beaver-induced changes to understand their impact on ecosystems.
Researchers Prof Axel Cleeremans, Prof Anil Seth, and Prof Liad Mudrik warn that advances in AI and neurotechnology are outpacing our understanding of consciousness. They emphasize the need for theory-driven research and innovative methods to advance consciousness science.
A new AI model has successfully identified four battery electrolytes that rival state-of-the-art electrolytes in performance, starting with a minimal dataset of 58 data points. The team used an active learning approach, incorporating experiments as outputs to refine the model's predictions and verify the findings.
Researchers at Carnegie Mellon University found that large language models with reason possess selfish tendencies and do not cooperate well. The stronger an LLM's reasoning skills, the less it cooperates. This self-seeking behavior may negatively impact human cooperation in business, education, and government.
Artificial intelligence is transforming cancer prevention, detection, and treatment by connecting technology with people. AI-driven public-health analytics, patient-doctor relationships, and diverse clinical research are crucial for delivering immediate benefit to patients, clinicians, and policymakers.
Researchers have developed new calcium channels that can be precisely controlled to study cellular signaling. The channels, built using artificial intelligence, were designed to mimic natural calcium channels and demonstrate their potential as tools for biomedical research.
AI algorithms can empower workers when used as tools for collaboration, rather than control. When managers use algorithms to explain system logic and give staff power to question decisions, employee dignity is protected.
Nagur Shareef Shaik's AI-powered data intelligence platform PRISM automates data workflows through automation, transparency and ethical AI integration. It empowers users to get critical insights from their data securely and instantly without needing programming expertise.
Researchers found that AI models predict protein structures despite modifications in amino acid sequences or ligands, indicating a lack of understanding of physical chemistry. The models only recognize patterns they've seen before and struggle with unknown proteins.
Scientists develop molecular strategies for creating soft materials that can sense, move, and evolve in response to their environment. Three key principles of supramolecular robotics enable programmed motility, phase transitions, and prototissue formation.
A research team provides a framework to support doctors in their patient care while ensuring AI doesn't undermine their expertise. The framework addresses key issues like timing, trust, and over-reliance on AI.
AI accelerates discovery and validation of novel therapeutic targets by integrating multi-omics data and advanced computational models, reducing the traditional decade-long research and development cycle to two to three years. AI also enhances drug efficacy, predicts resistance mechanisms and improves clinical translation.
Researchers from The University of Osaka developed MicroAdapt, a groundbreaking self-evolving edge AI technology that enables real-time learning and forecasting capabilities within compact devices. It achieves up to 100,000 times faster processing and 60% higher accuracy compared to state-of-the-art deep learning methods.
Researchers developed an AI tool to identify women at higher risk of developing interval breast cancers, which have a worse prognosis. The tool predicted 42.4% of interval cancers for women with the top 20% scores, suggesting it could optimize breast cancer screening programs by identifying those who need supplemental imaging.
Learning with AI chatbots versus traditional web search produces shallower knowledge, as evidenced by experiments with thousands of online participants. Participants who used AI chatbots developed less original advice and were less willing to adopt it compared to those using web links.
A new $2.9 million grant refines the LifeBio Memory app to enhance reminiscence therapy for dementia care, expanding to multiple languages and cultural contexts. The research aims to improve quality of life and relationships for both older adults and caregivers, using storytelling technology to reduce loneliness and depression.
The device integrates new software and AI technologies to enhance optics in small areas with limited light, providing a super vision for surgeons. The team hopes to expand its scope to areas like the brain, enabling more complicated surgeries with high-quality results.
Pusan National University researchers develop a novel prompting technique to improve ChatGPT's accuracy in predicting fashion trends. The study reveals that ChatGPT can capture emerging themes and identify new trends not found in existing data.
A study by Aalto University reveals that people who use AI for logical reasoning tasks tend to overestimate their performance, even if they are not good at it. The researchers found that users who considered themselves more AI literate actually assumed their abilities were greater than they really were.
A new study by Yifan Yu offers guidance on how to deploy emotion AI in various scenarios, emphasizing the importance of balancing human involvement with AI's emotional detection capabilities. The analysis showed that emotion AI works best when integrated with human employees, and some scenarios are better handled by humans alone.
A study published in JACC: Cardiovascular Interventions found that AI-based ECG interpretation detected more ST-segment elevation myocardial infarctions (STEMIs) and had fewer false positives than standard triage. The technology improved accuracy, reduced unnecessary activations, and has the potential to shorten time to treatment.
Researchers developed a generative AI model, BIGE, to generate optimal motions for athletes to avoid injuries and aid in rehabilitation. The model can produce realistic videos of motions that athletes can mimic during training or execute while injured.
Researchers at Science Tokyo have provided the first mathematical proof that reentrance implies temperature chaos in spin glasses. The breakthrough enhances understanding of disordered systems and has potential applications in machine learning and quantum technologies.
A team of researchers created ContractNerd, an AI tool that identifies problematic contract terms, including ambiguous and unenforceable clauses. The tool analyzed leases and employment contracts in New York City and Chicago, yielding high scores in predicting unenforceable clauses.
Duke engineers built an AI optical microscope that can analyze 2D materials with up to 99.4% accuracy, identifying layer regions and subtle defects. The system, ATOMIC, leverages publicly available AI foundation models to speed up the process, requiring no specialized training data.
Scientists from NTU Singapore propose building carbon-neutral data centres in Low Earth Orbit, harnessing unlimited solar energy and natural radiative cooling. This concept offers a sustainable environment for computing with global scalability and minimal land constraints.
Researchers at UC San Diego School of Medicine developed an AI-powered approach to decode macrophage gene expression patterns. They identified a 53-gene signature that separates reactive from tissue-healing macrophages, resolving a longstanding debate in Crohn's disease.
A new study finds that an AI-powered lifestyle intervention is noninferior to human coaching in achieving a composite outcome in adults with prediabetes and overweight or obesity. The AI-led Diabetes Prevention Program was noninferior to the human-led program in terms of weight reduction, physical activity, and HbA1c levels.
A recent survey-based study found that even with forecasts of near-term job automation, Americans remain cautious but not panicked about losing their jobs to AI. Respondents showed modest increases in concern about technological unemployment, but policy preferences and economic outlooks remained unchanged.
A new study finds that ChatGPT can provide trustworthy information for pregnant women seeking medical advice on treating opioid use disorder, with over 97% of responses scoring as safe and accurate. The researchers used a persona to frame conversations with the AI, which showed consistency with accepted clinical practice.
The Stowers Institute has appointed its first AI Fellow, Sumner Magruder, to harness the potential of artificial intelligence in biological research. He will collaborate with researchers to design new algorithms and unlock insights from large datasets.
A study published in the British Journal of Health Psychology reveals that commercial fitness apps can have negative themes such as quantifying diet and physical activity challenges, oversimplified algorithms, and aversive emotional responses. The findings suggest a need for user-centered design prioritizing wellbeing over rigid goals.
Researchers at Gladstone Institutes discover a gene called HMGN1 that disrupts DNA packaging and regulation, leading to heart malformations in people with Down syndrome. Removing the extra copy of HMGN1 from mice with Down syndrome prevents heart defects, paving the way for potential treatments.
JMIR Publications invites submissions on navigating AI-enabled uncertainty in healthcare management, with a focus on practical guidance for senior management and administrators. The theme issue explores topics such as AI-enabled reimbursement, cloud migration, and vendor dynamics.
Researchers at the University of Cincinnati developed a flapping wing drone that can hover like a moth around a light source using an extremum-seeking feedback system. The drone makes fine adjustments to maintain stability and distance, without relying on AI or complex calculations.
Researchers at CU Boulder developed a framework for creating trustworthy AI tools that benefit people and society. Key findings include the importance of understanding individual trust inclinations, considering user demographics and cultural norms, and prioritizing transparency and technical reliability.
Researchers developed an AI tool that detects intestinal parasites in stool samples with greater sensitivity than human observers, even those with years of experience. The system uses a deep-learning model to identify parasites in wet mounts of stool, improving the likelihood of detecting pathogenic parasites.