Articles tagged with Artificial Intelligence
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.
A new study reveals that AI chatbots, even when prompted to use evidence-based psychotherapy techniques, systematically violate ethical standards of practice. The research highlights the need for thoughtful implementation of AI technologies and appropriate regulation and oversight in mental health settings.
Researchers develop geophysical-machine learning tool that estimates soil strength parameters using limited borehole data, enabling continuous subsurface characterization. The approach reduces the need for expensive and time-consuming drilling in challenging terrains.
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.
Researchers at UC San Diego developed a new method for fine-tuning large language models with significantly less data and computing power. This approach updates only the necessary parts of the model, reducing costs and improving generalization.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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 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
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.
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.
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 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.