Articles tagged with Artificial Intelligence
Scientists have developed a simulation environment to test multistage attack scenarios using deep reinforcement learning, achieving success rates of up to 95% in stopping cyberattacks. The AI-powered approach offers promise for proactive cyber defense and the ability to detect changes in the cyber landscape earlier.
A recent study published in Human Reproduction found a significant association between prenatal alcohol exposure and changes in children's facial features. The research used AI to analyze three-dimensional images of children's faces at ages nine and thirteen, revealing that mothers' alcohol consumption before and during pregnancy can h...
Researchers at the University of Konstanz found that selfish behavior can lead to a fair distribution of risk within groups of animals. By positioning themselves optimally to minimize distance from others, individuals reduce their own predation risk and create a safer environment for all members.
Researchers at Cornell University developed an AI tool to track online debates and detect when tensions are escalating. ConvoWizard provides real-time feedback to users on potentially incendiary language, encouraging constructive debate.
A University of Kansas study analyzed over 1,000 AI-generated ads and found that they are only labeled as ads about half the time, often appealing to consumers in positive ways. The prevalence of AI in programmatic advertising raises concerns about consumer deception and the need for updated guidelines to require transparency.
Researchers use movie clips to study mouse brain activity, revealing novel ways to diagnose learning and memory disorders. The discovery points to new ways to tackle Alzheimer's and improve artificial intelligence.
A new clinical trial will assess the efficacy of an AI-powered massage robot, EMMA, in managing chronic low back pain. The study aims to explore the effectiveness of robotic Tuina therapy in alleviating pain and improving health outcomes.
Researchers at MIT developed a technique to improve machine-learning models' reliability without requiring additional data or extensive computing resources. The method uses a simpler companion model to estimate uncertainty, enabling more effective uncertainty quantification.
A new study uses Fourier analysis to understand how deep neural networks learn complex physics. By analyzing the equation of a fully trained model, researchers were able to identify crucial information about how the network learns and generalizes. This breakthrough could accelerate the use of scientific deep learning in climate science.
Research from the University of Cambridge found that cinematic depictions of AI scientists are heavily skewed towards men, contributing to a cultural stereotype that may deter women from pursuing AI careers. The study highlights the need for more diverse representation in AI development.
Scientists developed an automated algorithm using machine learning to identify image content related to illegal wildlife trade in digital spaces. The method uses feature visualization and achieves high accuracy in distinguishing between natural and captive contexts.
A study published in PLOS Digital Health found that ChatGPT scored between 52.4% and 75.0% on the USMLE exams, exceeding the passing threshold of approximately 60%. The model demonstrated high concordance rates and produced significant insights for most responses.
Researchers at Brigham Young University have developed an AI algorithm that can accurately locate players and determine formations in football game footage. The system has achieved over 90% accuracy on player detection and 85% accuracy on formation identification, potentially eliminating the need for manual annotation and analysis.
The study uses artificial intelligence to generate new molecules with optimized properties, applicable to various industries. The method enables finding Pareto-optimal solutions, leading to more efficient materials for optoelectronics, solar energy, and other fields.
Researchers tested three common techniques to make algorithms fairer and found that one approach didn't reduce social norm bias at all. They proposed a new technique: a formula to directly measure social norm bias in an algorithm so it can be corrected. This bias can persist even after overt discrimination is removed.
A team of researchers developed a model-free approach using deep reinforcement learning to optimize estimation of multiple parameters in quantum sensors. The protocol achieved significantly better estimations compared to nonadaptive strategies, demonstrating enhanced performance in resource-limited regimes.
Researchers found pigeons can solve complex categorization tests through exhaustive trial and error, mirroring artificial intelligence's process. Pigeons' ability to memorize patterns and objects surpasses human performance in similar tasks.
FRIDA uses AI models similar to those powering tools like ChatGPT and DALL-E 2 to generate paintings based on user input. The robot's final products are impressionistic and whimsical, with bold brushstrokes that lack precision sought in robotic endeavors.
Engineers at the University of Waterloo developed AI technology to predict breast cancer treatment effectiveness, allowing doctors to prescribe personalized treatments and reduce unnecessary side effects. The new algorithm uses synthetic correlated diffusion imaging images to forecast patient responses to chemotherapy.
The Human Brain Project Summit 2023 will highlight the scientific achievements of the project at the interface of neuroscience and technology. Researchers will present cutting-edge research on European advances in neuroscience and applications in medicine and brain-derived technologies.
Researchers developed an AI algorithm to predict prime editing efficiency and accuracy, enabling faster and more precise genome editing. By analyzing a large data set of over 100,000 pegRNAs, the tool identifies key properties influencing prime editing success.
A new study reveals that online hate speech is frequently conveyed through subtle and modified wording, allowing it to evade detection. Covert methods of expression are being used by haters to avoid detection, highlighting the need for increased attention to implied ways of expressing hate.
Researchers developed a machine learning model using advanced 2D chemical descriptors to predict highly selective asymmetric catalysts without quantum chemical computations. The model demonstrated high accuracy in predicting catalyst structures and selectivity, outperforming existing methods.
A team of researchers has proposed a novel method for multi-label classification that enables continual learning from big data. The approach combines clustering and Bayesian methods to achieve high classification performance and adaptability in the face of new data.
A new approach to deep reinforcement learning demonstrates ability to stabilize large datasets used in AI models, which may lead to uncovering ways to arrest cancer development. The method has been successful in designing and refining existing therapies, with the next step being to use live cells.
Researchers have developed a compact silicon photonic compute engine capable of computing tiled matrix multiplications at a record-high 50 GHz clock frequency. This achievement promises to contribute significantly to data center cybersecurity and enables real-time threat detection for malicious packets.
The University of Alberta and Amii are recruiting 20 new Canada CIFAR AI Chairs to focus on groundbreaking AI research in health, energy, and Indigenous initiatives. This initiative aims to accelerate opportunities for responsible commercialization of AI, driving future economic growth.
A new study at Cornell University introduced an automated participant that periodically interrupts the conversation to give nonnative speakers a chance to speak. The AI bot increased participation from 12% to 17% of all words spoken, with nonnative speakers feeling valued and appreciated for their perspectives.
A recent study found that AI technologies exhibit more pronounced biases in facial age perception than humans, with exaggeration of smiling faces' aging effect and decreased accuracy for older adults. The research suggests these biases stem from visual cues rather than preconceptions.
The JIPipe software enables automated analysis of images generated in research without requiring programming skills. Users can create flowcharts and perform automatic image analyses using artificial intelligence.
The use of artificial intelligence (AI) and language models in writing manuscripts has sparked debates about the integrity of scientific publication. AI-generated content can lead to misinformation and compromised academic standards, posing significant risks to medical knowledge.
Researchers have developed an AI-based resource to assist individuals in identifying recommended actions based on their clinical profile and COVID at-home test results. The system uses a combination of symptoms and home tests to provide more accurate diagnoses and improve patient care.
UC San Diego scientists developed an AI-based strategy for discovering high-affinity antibody drugs, identifying a new antibody that binds 17 times tighter than an existing one. The approach accelerates the discovery process, reducing failure rates and enabling faster development of novel cancer and COVID-19 treatments.
A deep learning model distinguishes colon carcinoma from acute diverticulitis in CT images, improving diagnostic performance and reducing false-negative and false-positive findings. The model's sensitivity and specificity were significantly improved, supporting radiologists in patient care.
Researchers identified a panel of novel blood biomarkers associated with sudden cardiac arrest, which have the potential to enhance clinical prediction. Additionally, an AI-powered ultrasound technology can accurately identify coronary artery calcium buildup, potentially replacing CT scans as a diagnostic tool.
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.