Articles tagged with Artificial Intelligence
Scientists developed an AI system, ProGen, that can generate artificial enzymes from scratch, working as well as those found in nature. The AI model learned aspects of evolution and was able to tune its generation for specific effects, creating proteins with unique properties.
Researchers at NYU Langone Health and the University of Toronto have developed a new AI tool called ZFDesign, which enables customizable protein editing for treating genetic diseases. The tool promises to accelerate gene therapy development on a large scale, offering a potentially safer alternative to CRISPR.
Researchers are using AI to assess soil datasets and measurements, aiming to build an automated data-driven decision support system for European soils. The goal is to create an effective soil health certification system that can be used by farmers, landowners, and policy makers.
Researchers developed a computational analysis method to detect and identify somatic SVs in leukemia patients, gaining insights into molecular consequences and potential therapies. The approach enables understanding of individual somatic mutations and may lead to targeted treatments.
Lithuanian researchers have developed an AI system to detect early symptoms of Parkinson's disease using voice data. The study found that patients with early-stage Parkinson's often speak in a quieter, more monotonous tone, which can be difficult to notice by ear.
Researchers developed an AI system that suggested changes to participants' responses, making them more empathetic. The system helped people communicate empathy more effectively than traditional training did.
A new secure computation method is introduced, allowing for decentralized computation of encrypted data on a single server without significant computational cost. This method addresses drawbacks of existing methods, providing faster and more secure computations.
Researchers successfully applied AlphaFold AI to an end-to-end platform, discovering a novel target and developing a potent hit molecule for liver cancer. The study demonstrates the potential of AI-powered drug discovery to accelerate treatment development.
Researchers found that rephrasing original questions and using bot-specific wording significantly improved resolution rates. Upskilling individuals to effectively interact with chatbots can help bridge the digital divide.
Researchers used deep learning AI to analyze chest X-rays of patients with acute chest pain syndrome, identifying those at high risk for life-threatening conditions. The model significantly improved prediction accuracy beyond conventional clinical markers, allowing for more accurate triage and potential deferral of additional testing.
The field of intelligent computing has made significant progress, focusing on autonomous perception, information gathering, analysis, and reasoning. Key findings include the need to develop more flexible systems that can accommodate diverse forms of intelligence through computing.
Researchers use drone technology to detect changes in building facades and assess technical condition, enabling accurate classification and identification. The system can also be used for urban heritage site monitoring and planning urban development.
Researchers at Brookhaven National Laboratory have successfully discovered new materials using artificial intelligence and self-assembly. The AI-driven technique led to the discovery of three new nanostructures, expanding the scope of self-assembly's applications in microelectronics and catalysis.
A new study from Pusan National University explores the use of collaborative artificial intelligence models in fashion design, finding that human designs offer unique originality and creativity. The researchers propose a human-AI collaborative network to produce novel designs, which can also be used as a learning tool for non-experts.
A new AI tool called Sybil has been developed to predict the risk of lung cancer for individuals with or without a significant smoking history. It uses deep-learning models to analyze low-dose chest computed tomography (LDCT) scans and provides accurate predictions for up to six years.
A $749,960 NSF grant will develop AI-powered AAC devices to improve outcomes for individuals with limited speech and language. The technology aims to reduce the burden on users and enhance their participation in education and workforce activities.
A survey of researchers and the public reveals a high percentage are in favor of proceeding with neuroscience research and brain-related information use. The general public expresses hopeful feelings about this research, while major differences exist between the two groups on ELSI topics.
Researchers developed an innovative deep learning-based approach to classify epileptic seizures in near real-time, overcoming clinical limitations. The system uses action recognition with intelligent 3D cropping to distinguish between frontal and temporal lobes seizures or non-epileptic events.
Scientists have developed a new method to enhance electron-photon coupling, resulting in a hundredfold increase in light emissions. The approach uses a specially designed photonic crystal to produce stronger interactions between photons and electrons.
A new computer program, DeepMosaic, uses artificial intelligence to detect mosaic mutations in genetic sequences. This method enables accurate detection of mosaic mutations, which cause hundreds of unsolved and untreatable disorders, including epilepsy.
The article discusses the use of Rapamycin in the context of Pascal's Wager, a philosophical framework used to justify beliefs. ChatGPT's generative pre-trained transformer model provides an exhaustive research perspective on the pros and cons of taking Rapamycin for anti-aging purposes.
Researchers found that AI-assisted colonoscopy significantly improved adenoma detection rates in patients with Lynch syndrome, detecting flat adenomas more effectively than standard examinations. The study suggests that AI-assisted real-time colonoscopy is a promising approach to optimize endoscopic surveillance for LS patients.
A new AI tool developed by Brigham and Women's Hospital improves the accuracy of time-critical pathology diagnostics during surgery. The tool, which leverages deep-learning technology, translates frozen tissue samples into high-quality images, increasing diagnostic accuracy and reducing the need for lengthy laboratory tests.
A predictive model developed by University of Groningen researchers can identify cows at risk of dystocia before insemination. The model, which uses machine learning on a large dataset, suggests that it could roughly halve the risk of calving problems.
Researchers at Drexel University used GPT-3 to spot early signs of Alzheimer's in spontaneous speech, achieving 80% accuracy. The program analyzed word-use, sentence structure and meaning from transcripts to identify characteristic profiles of Alzheimer's speech.
The RITHMS project aims to enhance police forces' and customs authorities' capabilities in tackling illicit cultural trafficking with innovative technologies. The platform will utilize Social Network Analysis to identify organized criminal networks and provide valuable information to investigators.
A digital detection of dementia study aims to evaluate the practical use of an AI tool for early identification of Alzheimer's disease and related dementias. The study uses a passive digital marker and patient-reported outcomes survey, which have shown potential in detecting mild cognitive impairment and reducing healthcare costs.
Researchers created an AI platform called DystoniaBoTXNet that uses brain MRIs to identify patients who will respond to botulinum toxin treatment. The platform achieved a 96.3% accuracy rate in predicting treatment efficacy, offering clinicians a new tool for refining clinical decisions.
A recent study found that AI failed to pass a radiology qualifying examination, with an average accuracy of 79.5%. However, the researchers suggest that further training and revision could lead to improved results.
Researchers at Georgia Institute of Technology found that AI chatbots' positive emotional displays have no effect on customer service evaluations when the source is a bot, but improve when human agents exhibit emotions. The study suggests businesses should be cautious about equipping AI agents with emotion-expressing capabilities.
The UTSA-led Consortium on Nuclear Security Technologies (CONNECT) has received a five-year, $5 million grant from the U.S. Department of Energy's National Nuclear Security Administration. The program aims to educate and train the next generation of scientists and engineers in nuclear security, with a focus on underrepresented students.
Researchers are conducting on-site surveys and generating high-resolution damage maps for 20-square-mile region affected by the Category 4 storm. The goal is to inform protection efforts and help communities recover from the disaster.
Students from the WüSpace association are working on a project to develop intelligent sensors and algorithms for small satellites, aiming to detect dangerous approaches and prevent collisions. The project is funded by the German Space Agency and allows students to gain hands-on experience in space technology development.
The UTEP-led Computing Alliance will receive $4.8M from Google to improve diversity in computer science fields. The project aims to attract, prepare and support Hispanic students for graduate degrees, with initiatives including lab design, financial support and research collaborations.
Researchers developed an AI-based neural network to detect early knee osteoarthritis from x-ray images, matching doctors' diagnoses in 87% of cases. This method could help reduce unnecessary examinations, treatments, and even knee joint replacement surgery.
Researchers have developed a robot capable of sorting, manipulating, and identifying microscopic marine fossils. Forabot uses robotics and artificial intelligence to automate the tedious process of evaluating foram shells and fossils.
Researchers at Incheon National University have developed an IoT-enabled, real-time object detection system for autonomous vehicles. The YOLOv3-based model achieved high accuracy (>96%) in detecting 2D and 3D objects, outperforming other state-of-the-art detection models.
Researchers found that longer genes are linked to longer lifespans and shorter genes to shorter lifespans, with aging causing a shift towards short genes. This imbalance leads to subtle changes in thousands of genes across various tissues, suggesting systems-level changes contribute to aging.
Researchers at MIT have developed a scheme for private information retrieval that is about 30 times faster than other comparable methods. The technique enables users to search an online database without revealing their query to the server, with potential applications in private communication and targeted advertising.
Researchers explored how customers react to positive emotions from AI chatbots compared to human agents. They found that adding emotions doesn't necessarily improve service quality or customer satisfaction.
Researchers have developed a scaled-up version of a probabilistic computer using stochastic spintronic devices, suitable for combinatorial optimization and machine learning. The new design combines conventional semiconductor chips with modified spintronic devices, achieving massive improvements in throughput and power consumption.
Researchers at FAU and industry partners aim to create a universal radio adapter for seamless and secure operations through non-cooperative indigenous 5G networks. The project aims to reduce interception, disruption, or jamming of communications over 5G networks.
A massive collaborative study using federated learning developed a model that enhances identification and prediction of boundaries in three tumor sub-compartments without compromising patient privacy. The dataset, comprising 6,314 glioblastoma patients from 71 sites globally, is the largest and most diverse ever considered.
A team of researchers from the University of Pennsylvania has developed a new algorithm, metadynamics, that can navigate high-dimensional energy landscapes to find low-energy configurations. This breakthrough has the potential to revolutionize fields such as protein folding and machine learning.
A new AI evaluation framework, GOPHER, has been developed to assess the efficiency of genome analysis algorithms. The tool judges programs on their ability to learn genomic biology, predict patterns, handle noise, and provide interpretable decisions.
Researchers at University of South Australia have developed a non-contact system to accurately measure systolic and diastolic blood pressure using AI algorithms and a camera. The system achieved 90% accuracy in readings, outperforming existing methods.
A study led by Kyoto University researchers found that AI-generated haiku poems, created without human intervention, were often indistinguishable from those penned by humans. In contrast, human-AI collaboration produced more creative works.
A team of researchers developed a neural network that can analyze digital biopsy images to diagnose breast cancer, identifying the presence of PD-L1 protein. The AI system outperformed human pathologists in 70% of cases, with high accuracy rates and potential for personalized medicine.
Researchers used AI to design and test thousands of functional group patterns on a carbon nanotube pore, finding optimal arrangements that can filter out contaminants. The study demonstrates AI's potential in developing new types of water purification membranes.
A neural network trained using a diverse dataset outperforms conventionally trained algorithms by reducing bias in artificial intelligence. The use of images from low-resource populations boosts the object recognition performance of machine learning systems.
Researchers from Kaunas University of Technology and Lithuanian University of Health Sciences developed an AI-based system to evaluate the 'substitute voice' of laryngectomy patients, allowing for faster detection of pathologies. The Acoustic Substitution Voice Index (ASVI) uses acoustic parameters and artificial intelligence methods t...
Researchers propose six competency domains for AI use in primary care: foundational knowledge, critical appraisal, medical decision making, technical use, patient communication, and awareness of unintended consequences. These competencies aim to harness AI's potential for patient and societal benefit.
Concordia researchers identify vulnerabilities in smart inverters to cyberattacks, including reconnaissance, replay, DDoS, and Man-in-the-Middle attacks. These attacks can disrupt energy flow and create power oscillations, severely impeding microgrid functionality.
The researchers have developed an AI algorithm called M3GNet that can predict the structure and dynamic properties of any material. The algorithm was used to create a database of over 31 million yet-to-be-synthesized materials with predicted properties, facilitating the discovery of new technological materials.
Researchers at the University of Texas at Dallas have developed a computer-based platform for drug discovery using topological data analysis. The approach allows for virtual screening of thousands of compound candidates, narrowing them down to the most promising ones for laboratory and clinical testing.
A new study using artificial intelligence has found that a simple eye test can accurately predict the risk of heart disease. The researchers developed an algorithm that can analyze retinal images to assess cardiovascular health, providing a non-invasive alternative to traditional risk scores.
A new intelligent observer for Esports has been developed using a Mask R-CNN algorithm to learn from human spectating data. This approach overcomes limitations of existing automatic observers, which require extensive domain knowledge and cannot capture undefined events or changes in event significance.
Researchers developed a new machine-learning framework that enables cooperative or competitive AI agents to consider the future behaviors of all agents, not just their teammates or competitors. This framework, FURTHER, uses two modules: an inference module and a reinforcement learning module, to enable agents to adapt their behaviors a...
Chugh argues that AI risk assessments raise concerns about human biases and the absence of individualized decision-making in the judiciary. She advocates for more research on AI's role in the court system, prioritizing community-driven and individualized processes.
Chung-Ang University researchers propose a new algorithm, MR-UCB and MR-APE, to tackle stochastic multi-armed bandit problems with heavy-tailed noise distributions. The methods guarantee minimal loss for worst-case scenarios with minimal prior information.