Articles tagged with Artificial Intelligence
A new study by Klick Labs reveals that AI technology can screen for Type 2 diabetes using six to 10 seconds of a person's voice, with high accuracy rates. The research used acoustic features to analyze recordings from over 18,000 participants and identified significant vocal variations between individuals with and without the condition.
The American College of Surgeons (ACS) Clinical Congress 2023 offers media credentials to cover groundbreaking research, connect with surgeon experts, and learn about the latest trends in surgery. Key topics include artificial intelligence, COVID-19's impact on cancer care, and pediatric trauma.
A new AI tool provides fully automated, easy-to-use evaluation of brain tumors using amino acid PET scans. The deep learning-based segmentation algorithm assesses treatment response with quality comparable to that of an experienced physician.
The High Performance Data Facility Hub will provide researchers with unprecedented data management resources, accelerating scientific discovery through seamless access to large and complex datasets. The hub will be led by Jefferson Lab and partner with Lawrence Berkeley National Laboratory.
Researchers from Mindgard and Lancaster University exposed vulnerabilities in major Large Language Models (LLMs) that can be copied for as little as $50. This 'model leeching' attack allows attackers to gain insights into how LLMs work, enabling them to launch targeted attacks with an increased success rate.
Northwestern University engineers developed a nanoelectronic device that can perform accurate machine-learning classification tasks in real time with reduced power consumption. The device can be deployed directly in wearable electronics for real-time detection and data processing, enabling more rapid intervention for health emergencies.
A new AI software has achieved a 100% detection rate for melanoma, significantly improving skin cancer detection accuracy. The software correctly identified 99.5% of all skin cancers and 92.5% of pre-cancerous lesions, with potential to reduce face-to-face consultations and enhance dermatology triage.
The Digital Twin Brain platform combines intricate brain atlases and dynamic neural models to simulate human brain functions. This innovative approach holds promise for advancing artificial general intelligence and precision mental healthcare.
Researchers developed an AI-based method to estimate BMD from plain X-ray images using a hierarchical learning framework. The approach showed high performance and reliability in estimating BMD, with correlation coefficients of 0.88 and 0.92 compared to DXA and QCT.
The author estimates that AI-related electricity consumption could increase by 85-134 TWh annually by 2027, comparable to the annual electricity consumption of countries like the Netherlands and Sweden. The growth highlights the need for mindful AI adoption to minimize energy-intensive applications.
Researchers developed AI tools to analyze speech patterns in people with schizophrenia and found that these patterns can be distinguished from those of healthy individuals. The study uses an AI language model trained on internet text to represent the meaning of words, and it was able to predict the words generated by control participan...
The study developed a highly accurate AI model for fully automated cancer detection, including small and difficult-to-detect tumors. The model could detect visually imperceptible cancer from normal-appearing pancreases substantially early before clinical diagnosis, with a median of 438 days.
A new generative model named scPoli enables multi-scale representations of cells and samples, facilitating the integration of high-quality large-scale datasets for novel biological insights and disease understanding. This model accelerates atlas building and usage, ultimately accelerating disease understanding and therapy development.
A new AI tool developed by Brazilian researchers can detect potentially cancerous lung nodules in CT reports, missing a crucial early diagnosis. The NLP tool achieved an accuracy rate of 97% in identifying suspicious nodules.
Post-Acute Sequelae of COVID-19 research aims to track long-term health symptoms in survivors. A $3.7 million grant will support the development of self-supervised deep learning technologies to recognize post-COVID lung progression phenotypes.
Researchers at the University of Texas at Austin developed an AI algorithm that accurately predicted 14 earthquakes within about 200 miles of their location and strength, with only one false warning. The system detected statistical bumps in real-time seismic data and paired them with previous earthquakes to make predictions.
A new generative AI model enhances clinical accuracy and textual quality of chest radiograph reports, surpassing traditional methods like teleradiology. Its implementation could enable timely alerts for life-threatening conditions, aiding imaging interpretation and documentation.
Researchers at BYU and Duke University found that AI can improve conversation quality and promote civil dialogue in online interactions. By using AI-generated rephrasings of users' messages, participants exhibited greater willingness to listen to opposing viewpoints and reported higher conversation quality.
Researchers developed a novel approach to predict therapeutic targets for aging and age-related diseases. They trained a domain-specific BioGPT model on biomedical literature, which improved its performance in identifying prospective targets.
Researchers at Argonne National Laboratory have developed an autonomous microscopy technique that uses AI to selectively target points of interest for scanning, speeding up the experimental process and allowing for greater efficiency in facilities with limited beam time. This innovative approach has the potential to accelerate scientif...
A coordinated network of space lasers could prevent collisions with manned and unmanned space assets by nudging debris off potential collision courses. The artificial intelligence-powered lasers can maneuver and work together to respond rapidly to debris of any size.
Researchers discovered that monk parakeets possess a unique tone of voice, known as a voice print, similar to humans. This finding raises the possibility that other vocally flexible species may also have a voice print.
Researchers found that humans can inherit AI biases after interacting with a biased system, leading to a permanent negative impact on human decisions. This effect was observed in three experiments, where participants who were aided by a biased AI system continued to replicate its bias even when unaided.
Researchers from the University of Tokyo have developed an AI-powered drone system that analyzes young plants to predict their expected growth characteristics. By identifying optimal harvest times, farmers can reduce waste and improve their income by up to 20.4%, while also benefiting consumers and the environment.
Researchers from SUTD successfully applied reinforcement learning to a video game problem, creating complex movement designs that outperformed top human players. The study's findings have the potential to impact robotics and automation, ushering in a new era of movement design.
Researchers discovered that users' prior beliefs about an AI chatbot's motives significantly impact their interactions with the agent. Priming users to believe certain things about the AI's empathy, neutrality, or manipulation influences their perception of its trustworthiness and effectiveness.
A study of 33 physicians across 17 specialties found that chatbots provided largely accurate information to diverse medical queries. However, the chatbots had important limitations that require further research and model development.
A new AI method leverages causal relationships in genome regulation to efficiently identify optimal genetic perturbations for cellular reprogramming. The technique reduces experimental costs by prioritizing the most informative interventions, leading to faster convergence and more effective results.
Researchers found that participants preferred 60-70 options from ChatGPT, citing high perceived accuracy and increased intent to purchase. This contradicts traditional choice overload theories, highlighting the impact of personalized recommendations on consumer decision-making.
GlowTrack, a non-invasive movement tracking method using fluorescent dye markers, improves the capture of diverse movements in laboratories. This technique enables easier comparison of movement data between studies, increasing scientific discovery and advancing fields like biology, robotics, and medicine.
The University of Trento coordinates a European research network, Elias, aiming to establish Europe as a leader in AI research. The project focuses on developing new computational systems and models for sustainable innovation, social cohesion, and reliable AI.
Researchers developed MonoXiver, a new method to help AI extract 3D information from 2D images, making cameras more useful tools for emerging technologies. The method significantly improves accuracy when used in conjunction with existing techniques, such as MonoCon.
Researchers identified potential therapeutic targets for Alzheimer's disease and other conditions using a new approach combining AI-driven target identification with protein phase separation analysis. The study provides insights into the role of protein phase separation in human disease and its potential as a therapeutic target.
Researchers at the University of Zurich developed PlantServation, a method that enables scientists to observe plants with great precision using AI and machine learning. The technique allows for the analysis of millions of images taken from various weather conditions, providing insights into how plants respond to environmental factors.
The UW team's system uses self-deploying microphones to divide rooms into speech zones and track the positions of individual speakers. The robots disperse as far from each other as possible, allowing for greater sound control and isolating specific areas or separating simultaneous conversations.
The Tong test proposes five critical characteristics for AGI benchmarks: infinite tasks, self-driven task generation, value alignment, causal understanding, and embodiment. This innovative approach combines a general algorithmic testing paradigm with human-AI interaction-based testing.
Scientists developed computational eye models to help patients and surgeons select ideal intraocular lenses and predict visual outcomes. The technology uses anatomical information of the patient's eye to provide guidance on expected optical quality post-operatively.
A comprehensive survey of ChatGPT papers reveals a rapid increase in interest for NLP models, but also raises concerns about biased content generation and technology misuse. The study emphasizes the need to establish responsible development and deployment frameworks for ChatGPT.
A new deep-learning model was developed by Professor Sanghyun Park's research team at DGIST to classify brain waves with high accuracy using only a small amount of data. This model is capable of overcoming the limitations of existing classification models and can be used for future research related to brain waves.
Researchers at U of A are developing an AI-powered system to analyze cardiac images, enabling clinicians to diagnose heart disease more accurately. The project aims to reduce healthcare costs and improve patient outcomes, with potential global implications.
Researchers used AI to predict 3D shapes of over 215 million proteins, providing insights into their functions and evolution. The Protein Universe Atlas offers a valuable resource for scientists to explore protein diversity.
A study funded by a $180,000 grant from the National Science Foundation will investigate sleep and sleep disorders using RFID tags attached to clothing. The research aims to detect abnormal body movements that may indicate health problems, such as falls in elderly individuals.
The Open Encyclopedia of Cognitive Science (OECS) is a dynamic web reference that will equip readers with essential tools to grapple with the profound implications of cognition and intelligence in today's society. With generous funding from James S. McDonnell Foundation and the Allen Institute for AI, the first set of articles will be ...
The project aims to assess the operational resilience of microgrids on DoD installations and ships, using new operational resilience indexes developed by Lehigh University researcher Javad Khazaei. The team will develop a dashboard to monitor resilience indexes in real-time, providing recommendations for improving the systems.
A group of experts has developed a framework to research and apply brain-cell-based computer technology responsibly. The technology has significant promise for accelerating disease understanding but raises questions about the moral status of bio-computers.
Researchers will develop new ecological models to analyze data from camera traps, satellite images, and DNA sequences to monitor biodiversity changes around the world. The project aims to detect early warnings of species moving north due to climate warming, providing insights into mechanisms of negative impacts on biodiversity.
A new UTHealth Houston study reveals that implementing artificial intelligence-powered software can significantly reduce treatment time for stroke patients undergoing endovascular thrombectomy, with an average reduction of 11 minutes in time to thrombectomy initiation.
Researchers at Osaka University develop a method to train AI models using simulated city images, reducing the need for real data and saving human effort. The approach generates realistic images with accurate ground truth labels, addressing instance segmentation challenges.
A study published in Scientific Reports found that large language model AI chatbots can generate creative ideas at least as well as the average human in a divergent thinking task. However, human participants with the highest scores still outperformed the best chatbot responses, especially in categories such as fluency and flexibility.
An AI system by Lunit identified improperly positioned endotracheal tubes with high sensitivity and specificity, allowing for earlier repositioning and reducing complications. The study included 539 chest radiographs from three institutions and showed promising results in detecting ETT presence and improper position.
Researchers found that even top-performing AI models can mistake nonsense sentences for natural language, highlighting the need for improvement. The study suggests that comparing human language processing to AI's could provide new perspectives on how we think.
Researchers developed an AI foundation model for eye care that can identify sight-threatening eye diseases and predict general health. The model, RETFound, was trained on millions of eye scans from the NHS and outperforms existing state-of-the-art AI systems across complex clinical tasks.
A pilot study found that ChatGPT performed as well as doctors in suggesting the most likely diagnoses for patients being assessed in emergency medicine departments. The chatbot generated a list of likely diagnoses and suggested the most likely option, with a large overlap between its shortlist and those of doctors.
A new paper suggests that ecology can inspire more powerful, resilient, and socially responsible AI to solve complex problems. Artificial intelligence systems can incorporate greater amounts of data and diverse sources, discovering new interactions and drivers that may not have been considered before.
Researchers developed a method to predict rice yield using smartphone photos, showing 68% accuracy and comparable results to satellite data. The model's simplicity makes it suitable for mobile devices and can be easily transferred to other crops.
A study found that people perceive AI-assisted messages as less effortful and more improper than manually written messages. As a result, participants expressed lower satisfaction with their relationship and increased uncertainty about the friendship. Additionally, receiving an AI-aided response also led to negative perceptions of Taylo...
The article discusses how artificial intelligence can enhance climate modeling by generating large ensembles of simulations at moderately high resolutions. This approach capitalizes on advances in computing and AI to learn from data, enabling more accurate predictions and reduced uncertainty. By focusing on resolutions between 10-50 km...
A new study by USF researchers found that linguistics experts were unable to distinguish between AI- and human-generated abstracts less than 39 percent of the time. The study concluded that ChatGPT can write short genres as well as humans, while longer texts are easier to identify as AI-generated
Scientists are using AI to analyze pollen grains in sediments and air samples to understand past environmental change. The new system can classify over a thousand pollen grains in under an hour, providing insights into biodiversity and climate change.
A new AI tool assesses motor performance using finger taps, providing rapid and standardized ratings, comparable to expert neurologists. The test outperformed primary care physicians in accuracy, opening doors to evaluating other movement disorders.