Articles tagged with Artificial Intelligence
Researchers aim to bridge educational divide with a multifaceted approach to equip teachers and students with essential AI skills. The project will foster proficiencies in communication, leadership, teamwork, and critical thinking while addressing real-world challenges through AI.
Researchers at Washington State University developed an AI algorithm that optimizes 3D printing settings, reducing time and cost for engineers. The algorithm improved the accuracy and quality of printed models, particularly for complex biomedical devices like kidneys and prostates.
Researchers at Uppsala University used AI to predict the three-dimensional structure of a receptor, identifying molecules that bind to it with higher accuracy than traditional methods. This breakthrough accelerates the development of new drugs for mental health disorders such as schizophrenia and depression.
A commercial AI tool was effective in excluding pathology and had lower rates of critical misses on chest X-rays compared to radiologists. The study found that the AI tool could autonomously report more than half of all normal chest X-rays without decreasing standard of care.
Researchers at USC developed an approach that matches polysomnography using a single-lead echocardiogram, allowing anyone to create their own low-cost, DIY sleep-tracking device. The software significantly outperformed other EEG-less models and assesses sleep stages at the highest level.
Researchers developed Porous-DeepONet, a deep learning framework that efficiently captures complex features of porous media and learns solution operators. It outperforms traditional FEM methods in solving parameterized reaction-transport equations in porous media, handling complex domain geometries and multiphysics coupled equations.
A recent study by Columbia University Mailman School of Public Health found that Generative artificial intelligence (LLM) can't yet reliably read and extract information from clinical notes in medical records. The AI, ChatGPT-4, performed well only when given highly detailed prompts and struggled to replicate its work across trials.
Researchers have identified dozens of potential new antibiotics in the human gut microbiome, with one candidate showing promise against multidrug-resistant infections. The discovery uses artificial intelligence to analyze vast amounts of biological data and mine the world's biological information as a source of antibiotics.
Researchers found large language models are more accurate with concise, textbook-like medical questions than patient-written summaries. The models achieved higher accuracy when using standardized language, but struggled with variable phrasing and format of patient write-ups.
Researchers at Florida State University have developed a statistical analysis method that can detect when AI chatbot ChatGPT is used to cheat on multiple-choice chemistry exams. The study uses Rasch modeling and fit statistics to identify patterns in student responses that are different from those of ChatGPT.
Researchers developed a new brain-computer interface that translates brain signals into speech with up to 97% accuracy, enabling a man with amyotrophic lateral sclerosis (ALS) to communicate with friends and family. The system was tested in real-time conversations with continuous updates, achieving high word accuracy rates.
Researchers developed a framework called SigLLM that uses large language models to detect anomalies in time-series data. The approach converts time-series data into text-based inputs and can be deployed right out of the box, offering an efficient anomaly detection solution for complex systems.
A Chinese Medical Journal study developed an AI-based system to automate embryo selection and eliminate subjectivity in IVF. The system improved human embryo assessment and selection, achieving higher accuracy in embryo aneuploidy screening than experienced embryologists.
The University of Kansas study suggests that educational systems must undergo transformation to fully utilize the potential of artificial intelligence tools. Personalized learning and project-based learning are key strategies for harnessing AI's transformative power, enabling students to focus on their strengths and interests.
The Center will develop a For-Virginia AI Strategy, track business AI adoption rate, and offer programs to SMEs to understand and capitalize on emerging AI technologies. The project aims to bolster economic competitiveness of small and medium enterprises across Virginia.
Researchers at University of Bath and Technical University of Darmstadt found that large language models like ChatGPT cannot learn independently or acquire new skills, making them controllable and predictable. The study concluded that LLMs remain inherently safe, but misuse is still possible.
A Mass General Brigham study highlights inconsistencies in generative AI that can affect patient safety if not addressed. The researchers found 'drift' (model performance changes over time) and 'nondeterminism' (inconsistent results between runs) in their tests, emphasizing the need for repeated testing and monitoring.
Researchers developed a learning-based approach called LVWS to schedule robots and enable voluntary waiting, resulting in faster task completion. This method outperformed other approaches by up to 23% suboptimality, demonstrating its potential to improve manufacturing, agriculture, and warehouse automation.
Researchers at Carnegie Mellon University propose guidelines for using interpretable machine learning methods in computational biology to tackle complex problems. The guidelines address pitfalls such as relying on a single method and cherry-picking results, emphasizing the need for multiple approaches and human-centric considerations.
New research finds that AI explanations can fuel a perception of fairness without being grounded in accuracy or equity. Humans were more likely to override AI recommendations when explanations highlighted gender rather than task-relevance, but this did not improve decision-making accuracy.
A new deep learning-based inverse design method allows for the optimization of complex acoustic metamaterials, reducing noise pollution while maintaining ventilation. The approach enables ultra-broadband sound attenuation across various peak frequencies.
A survey by researchers at the University of Washington found that creating and sharing synthetic media is widely considered unacceptable, but seeking out such content is less so. The study highlights the need to expand norms around consent and privacy in the face of emerging AI technologies.
Researchers found that when participants were told their decisions would train an AI bot, they became more likely to seek a fair share of the payout. This behavior persisted even after the experiment ended, suggesting a lasting impact on decision-making.
Researchers have developed AI systems that use photos or videos to create simulations for training robots in real settings. The RialTo system generates highly accurate simulations of specific environments, while the URDFormer system creates generic simulations quickly and cheaply. These advancements aim to lower costs and increase acce...
A recent study found that generative AI images of physicians exhibit demographic biases, with a disproportionate representation of white and male physicians. The study highlights the potential for these biases to reinforce stereotypes and undermine diversity initiatives in healthcare.
A recent study published in JAMA Network Open demonstrated the effectiveness of AI in detecting myopia, strabismus, and ptosis using smartphone images. This technology has the potential to facilitate early detection of pediatric eye diseases in a convenient and accessible manner.
A new study led by CU Boulder computer scientist Theodora Chaspari found that AI algorithms can be confused by natural variations in speech patterns between different genders and races. This can lead to underdiagnosis or misdiagnosis of mental health concerns like depression.
Researchers developed a real-time stream-based data compression technology that automatically detects frequently occurring data patterns and compresses them to minimize data volume. This innovation enables high-speed, compact data compression modules in hardware without additional processors or memory.
A new study found that hospital pneumonia diagnoses are often uncertain and revised, with over half of all cases involving a change in diagnosis. This uncertainty can lead to poorer health outcomes for patients who initially lack a pneumonia diagnosis but later receive a diagnosis.
Researchers at FAMU-FSU College of Engineering have developed an AI-powered tool to train surgeons by analyzing video of their surgical technique and providing feedback. The system uses deep learning models to evaluate surgical skills effectively, offering real-time feedback for aspiring surgeons.
A team of scientists from Scripps Research has developed an AI tool that can diagnose heart conditions, including heart attacks, using just three electrodes and a simpler electrocardiogram technology. The tool was tested on 238 ECGs and showed accurate clinical assessment results similar to those from original 12-lead ECGs.
A team from Doshisha University has developed two approaches to generate adversarial examples for image cropping, achieving significant reductions in perturbation sizes. The white-box approach manipulates gaze saliency maps to produce effective images, while the black-box approach uses Bayesian optimization to target specific regions.
A novel AI tool has been shown to accurately estimate gestational age from blind ultrasound sweeps, comparable to expert sonographers. The technology has the potential to democratize prenatal care in resource-limited settings by expanding access to quality diagnostic tools without the need for expensive equipment or specialized training.
The Chameleon testbed has secured $12 million in funding to expand its services in computer science research. With this new funding, the platform will continue to innovate and support its growing community, enabling groundbreaking discoveries in CS systems research.
A study published in PLOS ONE analyzed 24 state-of-the-art Large Language Models (LLMs) and found that most produced responses rated as left-of-center when asked politically charged questions. The researchers used various tests, including the Political Compass Test and Eysenck’s Political Test, to evaluate the LLMs’ political orientation.
A new AI protocol called DeepHRD enables accurate, instantaneous detection of cancer genomic biomarkers directly from tumor biopsy slides. This breakthrough technology saves weeks and thousands of dollars from clinical oncology treatment workflows, providing access and equity in cancer care for resource-constrained settings.
The EMERGE project aims to demonstrate a new framework for coordinating artificial systems and humans. Shared awareness enables simpler AI systems to work together effectively, reducing energy costs and increasing efficiency.
A new study found that an AI program generated explanations of heart test results in most cases, with 73% deemed suitable for patients without changes. The AI explanations were also rated as easy to understand by non-clinical participants, reducing worry in many cases.
Researchers evaluate ChatGPT as a diagnostic tool for medical learners and clinicians, finding it reliable only half the time. The study warns against relying on the AI for medical advice due to its limitations.
Researchers have introduced a new AI calibration method called Thermometer, which enables efficient calibration of large language models for various tasks. This technique leverages temperature scaling to adjust a model's confidence and can generalize to new tasks without requiring additional labeled data.
A new study uses artificial intelligence to predict which breast cancer patients are most at risk for developing chronic pain, with anxiety and depression being leading factors. The AI model was built with detailed data on over 1,000 breast cancer patients and correctly predicted chronic pain more than 80% of the time.
A recent study published in the Journal of Hospitality Marketing & Management found that including artificial intelligence in product descriptions reduces purchase intentions. Emotional trust plays a critical role in how consumers perceive AI-powered products, and mentioning AI can lower trust levels.
A collaboration of scientists, ethicists, and researchers aims to create a consensus definition for diverse intelligent systems, including AI, LLMs, and biological intelligences. The proposed approach will provide a common language for recognizing, predicting, manipulating, and building cognitive systems.
A recent study at Rice University found that using synthetic data to train generative AI models can lead to negative consequences, including model collapse and reduced quality. As models become increasingly dependent on self-consuming loops, they may produce warped outputs lacking diversity or quality.
A study of Reddit comments on ChatGPT's launch reveals the tech community is more strongly divided in their opinions about AI. While non-tech discussions focus on social issues like job replacement, tech-centric subreddits have more focused and opinionated debates.
A new AI-powered image recognition technique could help scientists detect dark matter at the LHC by flagging fleeting tracks before collisions occur. The technique, developed by Ashutosh Kotwal and his team, processes images in under 250 nanoseconds and weeds out uninteresting data points.
A new device called computational random-access memory (CRAM) reduces energy consumption for artificial intelligence applications. CRAM enables true computation in and by memory, breaking down the bottleneck in traditional computing architecture.
A new study published in Nature Medicine found that people rate medical advice as less reliable and empathetic when an AI is believed to be involved. Despite this, respondents were still less willing to follow AI-supported recommendations compared to advice from human doctors.
Researchers at the University of Missouri are developing a two-phase cooling system that efficiently dissipates heat from server chips through phase change. This innovative system drastically reduces the amount of energy needed to keep equipment cool, with early tests showing significant reductions.
A new AI model can identify certain stages of ductal carcinoma in situ (DCIS), a type of pre-invasive breast cancer, that are likely to progress to invasive cancer. The model uses imaging and machine learning to analyze tissue samples and determine the stage of DCIS based on cell arrangement and organization.
Researchers at UVA School of Engineering and Applied Science developed artificial compound eyes that mimic praying mantis vision, offering improved depth perception and reduced power consumption by over 400 times compared to traditional systems.
Researchers found that large language models perform poorly in high-stakes situations despite being better than smaller models, due to misalignment with human generalization function. Human generalization, which involves forming beliefs about others' abilities, plays a significant role in LLM performance and deployment.
Researchers at NIH found an AI model solved medical quiz questions with high accuracy, but made mistakes explaining images and reasoning behind diagnoses. Human physicians scored higher when using external resources.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
The KEEPCARING study aims to develop a digital tool using AI to predict the most effective stress-reducing methods for individual healthcare providers. Researchers will also map and use existing stress measurement methods, and test interventions in clinical trials across multiple European hospitals.
The study explored how AI can be designed using a collaborative design strategy with input from primary care team members to better capture social determinants of health in clinical settings. Key findings suggest that the integration of AI into healthcare has both opportunities and challenges.
A study found that large language models, despite accuracy in medical exams, fail to consistently request necessary examinations and often deviate from treatment guidelines. In comparison to human doctors, AI diagnoses achieved lower accuracy rates, highlighting concerns about their suitability for everyday clinical practice.
Researchers at Duke University have broken through the performance wall of adaptive radar systems using convolutional neural networks, paralleling computer vision. They've released a large open-source dataset for other AI researchers to build upon their work, aiming to tackle industry needs like object detection and tracking.
A randomized crossover trial found that a joint-AI model improved clinical diagnosis, but future trials are warranted to confirm its effectiveness. The AI model demonstrated positive human-AI interaction, suggesting its potential to facilitate accurate diagnoses.
A new study uses machine learning to analyze the genetic diversity of two amphibian species, finding that different processes shaped their evolution. The research suggests that population demographic events and contemporary landscape factors played a significant role in shaping the genetic variation of these species.