Articles tagged with Algorithms
Researchers from McGill University and MIT developed an AI system that can learn the rules and patterns of human languages on its own. The model automatically generates higher-level language patterns that can be applied to different languages, achieving better results.
Researchers at North Carolina State University developed a blueprint for incorporating ethical guidelines into AI decision-making programs. The new mathematical formula, based on the Agent, Deed, and Consequence (ADC) Model, considers intent, character, and consequences of actions to make more informed decisions.
Researchers developed a smart mouthguard that translates complex bite patterns into instructions to control devices such as computers, smartphones and wheelchairs. The device achieves 98% accuracy and has the potential to support individuals with limited dexterity or neurological disorders.
New research highlights the dangers of AI-powered recruitment tools that claim to remove discrimination from hiring. The tools reduce race and gender to trivial data points and often rely on personality analysis that is
Researchers found that machine learning models outperformed traditional risk prediction models in predicting suicide-related outcomes. These models can identify patterns associated with suicide risk and have been shown to correctly predict 66% of people who would experience a suicide outcome.
Researchers used machine learning algorithms to optimize climate models, increasing their accuracy and detail. By applying Generative Adversarial Networks (GANs) to climate simulations, the team was able to improve the models' ability to represent extreme precipitation events.
A new study proposes a powerful computer-modeling approach to cell simulations, reaching unprecedented simulation timescales at all-atom resolution. The technique combines advantages of protein docking and molecular simulations, enabling faster and more precise treatment of human disease.
Researchers developed a universal screening tool for IPF that can alert primary care physicians to its possible presence, enabling earlier diagnosis and treatment. The Zero-burden Co-Morbidity Risk Score for IPF (ZCoR-IPF) algorithm uses existing patient records to identify patients at risk of developing the disease.
A new automated screening tool can accurately identify patients at high risk of developing progressive scarring of the lungs, a condition called idiopathic pulmonary fibrosis (IPF). The tool uses machine-learning algorithms to analyze patient electronic health records and detects IPF risk automatically.
Neuronal silencing periods enable efficient temporal sequence identification, allowing the brain to remember phone numbers and PINs. A new AI mechanism utilizing this mechanism also protects against stolen cards by recognizing personal handwriting style and timing.
A study published in Cell Press found that when humans are involved, computer decisions are perceived as fairer. Participants deemed decisions related to positive outcomes fairer than negative ones and had concerns over fairness in systems with higher stakes. The results suggest that automated decision-making systems need careful desig...
Researchers developed a computational platform to identify metabolic vulnerabilities in ovarian cancer genes, suggesting opportunities for targeted therapies. The study found that certain genetic alterations can create vulnerabilities in cancer cell metabolism, which can be exploited to selectively kill cancer cells.
Researchers developed an algorithm to decode brain scans and identify epilepsy types based on electrical signal patterns. The Cumulative Sharp Count and areas under spike and sharp curves were used as parameters to detect epilepsy, with high accuracy rates in blind validation studies.
Researchers developed an AI tool using natural language processing and machine learning to identify people who inject drugs in electronic health records. The model accurately identified PWIDs in 1,000 records from 2003-2014, significantly improving clinical decision making and resource allocation.
A study from the University of Georgia shows people who rely on algorithms for creative tasks don't improve their performance and are more likely to trust low-quality advice. Participants preferred algorithm-derived advice over human-based advice, even when confident in their answers.
Researchers have developed an algorithm that uses smartphone camera and flash to detect low blood oxygen levels. The method produced accurate results in 80% of the participants, showing promise for remote monitoring and early detection of conditions like COVID-19.
A large-scale experimental study by Harvard, Stanford, and MIT researchers found that weaker social connections on LinkedIn have a greater beneficial effect on job mobility than stronger ties. Weaker ties increased the likelihood of job mobility the most, while strongest ties had the least impact.
Healthcare researchers caution against misusing AI algorithms in clinical research, highlighting concerns about bias, transparency, and data quality. The team advocates for evaluating ML methods against traditional statistical approaches and ensuring clinician decision-making is complemented, not replaced.
A new study from MIT reveals that computer models predicting molecular interactions, like AlphaFold, need improvement to help identify drug mechanisms of action. Researchers improved the performance of these models using machine-learning techniques, but more work is needed.
A new study by New York University found that YouTube's recommendation algorithm prioritized election-fraud-related videos for users already skeptical about the 2020 presidential election's legitimacy. This highlights the dangers of opaque algorithms perpetuating misinformation and disinformation.
Researchers at Chalmers University of Technology developed a computer model to predict enzyme efficiency. This helps find efficient cell factories for producing biotech products like biofuels and medicines, and studies difficult diseases.
Xiu Yang, a 2022 NSF CAREER award recipient, is working on an algorithmic approach to model and overcome hardware errors in quantum computing. He aims to enable the technology to achieve its promise of unparalleled speed in solving complex problems.
A Brazilian research team has developed a novel method to sort specialty and standard coffee beans using multispectral imaging and machine learning. The technique, which does not require roasting or human intervention, uses images of the beans at different wavelengths to distinguish between quality levels.
Researchers designed a centered error entropy-based sigma-point Kalman Filter to enhance the filtering algorithm's robustness in spacecraft attitude determination. The proposed CEEUKF outperformed classical methods and other robust algorithms in simulating non-Gaussian noise, achieving higher accuracy and faster convergence rates.
Rice University's ROBE Array algorithm slashes the size of DLRM memory structures, allowing training on 100 megabytes of memory and a single GPU. The method matches state-of-the-art DLRM training methods with improved inference efficiency.
A team of Japanese researchers used reinforcement learning to study fluid mixing during laminar flow, achieving exponentially fast mixing without prior knowledge. The method also enabled effective transfer learning, reducing training time for new mixing problems, and has potential applications across various industries.
Researchers at the University of Oldenburg and Fraunhofer IWES collaborate on a new project to develop more accurate wind flow simulations using artificial intelligence. The goal is to reduce computing times and enhance precision, ultimately accelerating innovation in wind turbine design.
Researchers at MIT developed an AI model that can detect Parkinson's disease from breathing patterns, using a neural network to assess the presence and severity of the condition. The device is non-invasive and can be used in patients' homes without any bodily contact.
Researchers at NC State University have developed a cooperative distributed algorithm that allows autonomous vehicle software to make calculations more quickly, enabling real-time navigation of complex merging scenarios. The approach improves both traffic flow and safety, with zero incidents in simulations.
Scientists at Kyoto University developed two methods to identify RNA modifications impacting gene regulation and disease. Their approach uses probability algorithms with high-throughput sequencing technology, distinguishing pseudouridine substitutions from other base changes.
Bhattacharya's project uses topological abstraction to reduce complexity in robotic systems, enabling more efficient and accurate motion planning. The approach has potential applications in industries such as transportation, manufacturing, and healthcare.
Researchers at Princeton University used artificial intelligence to simulate ice formation by individual atoms and molecules with quantum accuracy. This breakthrough enables tracking of hundreds of thousands of atoms over longer timespans than previous simulations.
Researchers used natural language processing and machine learning to analyze nearly 35,500 death records, identifying the most common substances involved in overdose deaths. The system reduced data processing time by months, allowing for more rapid public health responses and interventions.
Researchers used machine learning algorithms and k-D tree data structure to identify 11 previously undetected space anomalies, seven of which are supernova candidates. The team analyzed digital images of the Northern sky taken in 2018 using a k-D tree to detect anomalies through the 'nearest neighbour' method.
Researchers optimized the ZZ SWAP network protocol, introducing a new technique to improve quantum error mitigation. This enables more efficient execution of quantum algorithms like QAOA, which can solve combinatorial optimization problems.
Researchers argue that using tools to estimate racial and ethnic information can identify algorithmic bias and combat disparities in healthcare. This approach can lead to more equitable pay-for-performance schemes and better clinical decision-making.
A team of Chan Zuckerberg Biohub scientists developed a deep-learning method, dubbed
A University of Washington team created a new tool that can design a 3D-printable passive gripper and calculate the best path to pick up an object. The designed grippers and paths were successful for 20 out of 22 objects tested, with two challenging shapes being the wedge and pyramid shape.
A new AI program identified four variables for a swinging double-pendulum, but the remaining two variables remain a mystery. The AI successfully predicted physical phenomena in other systems, such as air dancers and lava lamps, with varying numbers of variables.
A new AI algorithm, IcePic, has been developed to predict ice crystal formation with high accuracy. It outperformed human scientists in an online quiz, identifying areas where humans were wrong and providing valuable insights for atmospheric science research.
A new method can improve explosion detection by training computers to recognize synthetic infrasound signals, which reflect regional and global atmospheric changes. This approach broadens the usefulness of single-element infrasound microphones for detecting subtle explosion signals in near real-time.
Researchers at Max Planck Institute for Intelligent Systems created a robot dog named Morti that can walk smoothly within an hour. The robot uses a Bayesian optimization algorithm to learn from sensor data and adapts its virtual spinal cord, allowing it to optimize its walking pattern and minimize stumbling.
A study by Carnegie Mellon University researchers found that algorithmic transparency can have positive effects for firms, allowing them to motivate agents to improve valuable features. However, transparency may not always be beneficial for agents, as it can lead to a loss of predictive power and disadvantage high-type agents.
Researchers created a machine-learning algorithm to predict neighborhood racial segregation, showing 86% accuracy in one test. The map analyzed census data and showed less segregated areas becoming more mixed by 2030.
Researchers used logistic regression and a recommendation algorithm to predict CME arrival times, achieving better results than using either method alone. The hybrid model improved forecast accuracy by providing a reference for similar historical events.
Researchers developed a neural network algorithm that recognizes emotions and engagement from video images of faces, outperforming existing models in accuracy. The system can be integrated into video conferencing tools and online learning systems to analyze participant engagement and emotions.
A team of researchers led by Danilo Vasconcellos Vargas has developed a new method called 'Raw Zero-Shot' to evaluate the robustness of artificial neural networks in image recognition. The study found that Capsule Networks produced the densest clusters, indicating improved transferability and potential solutions for improving AI robust...
Researchers developed an automated method to create 3D images of leaked gas clouds, enabling precise location, volume, and concentration determination. This technology can provide early leak warnings, assess risk, or determine the best way to fix leaks in large facilities with stored toxic chemicals.
A new robotic system, FuseBot, has been developed to efficiently retrieve buried objects in piles. The system uses radio frequency signals and computer vision to reason about the probable location and orientation of objects under the pile, enabling it to find more hidden items than a state-of-the-art robotics system in half the time.
Researchers developed open-source software SHRY to find distinct substitution patterns in disordered systems, reducing computation time. The software uses group theory and canonical augmentation to efficiently analyze crystal structures with random substitutions.
A newly expanded data set of brain scans from stroke patients called ATLAS now includes 1,271 MRI images with manually segmented lesions, facilitating large-scale stroke recovery research. Researchers hope to develop algorithms to automate lesion segmentation, enabling clinicians to predict patient responses to therapies.
A novel algorithm, BLIND, enables robots to navigate through environments with obstacles by incorporating human feedback. Humans provide labels to refine the robot's trajectory, avoiding obstacles efficiently.
Researchers found that people are less morally outraged when gender discrimination occurs due to an algorithm rather than direct human involvement. The study's findings have broader implications for efforts to combat discrimination and may affect how companies are held liable.
SeqScreen, an open-source software toolkit, accurately characterizes short DNA sequences to detect pathogenic sequences. The program uses a curated database of thousands of gene sequences representing 32 types of virulence functions.
A team of scientists has developed a novel computational approach to analyze DNA sequences of thousands of bacteria, revealing previously unknown gene clusters responsible for producing metabolites of interest. The study highlights the potential of these bacterial compounds in treating colon cancer and improving treatment options.
A study by Thorsten Lehnert shows that corporate managers' behavior is linked to investor sentiment, predicting investment strategy success. The researcher found a significant relationship between market-level euphoria and investment factor performance, outperforming static strategies.
Researchers developed an algorithm to improve matching efficiency, considering user preferences and behavior. The new algorithm shows improved results in field experiments, with at least 27% more matches than the current one.
Researchers at MIT identified a flawed analysis of website-fingerprinting attacks and developed new techniques to prevent them. They found that attackers can use machine-learning algorithms to decode signals leaked between software programs, enabling them to obtain private information.
A new training algorithm for deep spiking neural networks (SNNs) uses biologically plausible spatiotemporal adjustment to improve performance and reduce energy consumption. This approach achieves competitive classification accuracy with only 3% of the energy used by traditional artificial neural networks.
A team of researchers from Waseda University developed a novel solution to efficiently solve complex optimization problems using Ising machines. Their hybrid algorithm reduces residual energy and reaches more optimal results in shorter time, increasing the machine's applicability across industries and sustainability practices.