The BirdNET app, a free machine-learning powered tool that can identify more than 3,000 birds by sound alone, generates reliable scientific data and makes it easier for people to contribute citizen-science data on birds by simply recording sounds. Results of tests to measure the app’s accuracy are published in the open access journal PLOS Biology.
Yevgeniy Vorobeychik doesn’t know exactly what sentience is. Since he’s an engineer, not a philosopher, Vorobeychik can’t say what it’s like to be a bat or a tree or a rock. He can’t quantify the importance of embodiment to consciousness. He’s not even sure that there’s an inherent problem with people reacting to an artificial intelligence in ways similar to how they react to other people.
Mount Sinai researchers have developed a machine learning algorithm that can determine if an individual has SARS-CoV-2 infections, the virus that causes COVID-19—with a high sensitivity and specificity—from the data collected by wearable devices.
University of Florida scientists will study the use of artificial intelligence in assessing livestock mobility in order to identify complex locomotor issues faster and with more accuracy than the human eye, leading to improved farm animal health and production.
A multi-institutional team, including Argonne National Laboratory, has developed a material with which computer chips can be designed to reconfigure their circuits when presented with new information. It does so by mimicking functions in the human brain.
A sensor-equipped computer program can accurately identify and count arm movements in people undergoing stroke rehabilitation, a new study shows.
Researchers have pioneered a technique that can dramatically accelerate certain types of computer programs automatically, while ensuring program results remain accurate.
Using a combination of drones and machine learning techniques, researchers from The Ohio State University have recently developed a novel method for determining crop health and used it to create a new tool that may aid future farmers.
Nowadays doctors define and diagnose most diseases on the basis of symptoms.
Posture is an important part of health. Prolonged poor posture, such as slouching or leaning to one side, can lead to pain and discomfort.
What if we used TikTok as a tool to further scientific research? University of Minnesota computer science Ph.D. student Yasamin Jafarian is doing just that, using data from the app to create more realistic 3D digital avatars.
In a new study, an international team of researchers explored how crowdsourcing projects can make the most effective use of volunteer contributions.
A major donation has enabled the launch of an exciting new interdisciplinary collaboration at UT Austin involving the Oden Institute for Computational Engineering and Sciences, Machine Learning Labs and Dell Medical School.
The importance of sleep and dreams for learning and memory has long been recognized – the impact that a single restless night can have on our cognition is well known.
Hertz Fellows John Frank, Dan Roberts and Max Kleiman-Weiner cofounded Diffeo, an AI start up company later acquired by Salesforce, after meeting at the Hertz Summer Workshop.
Scientists at Argonne’s Advanced Photon Source have created a new method using artificial intelligence to speed up the analysis of X-ray diffraction data.
Chemists at the U.S. Department of Energy’s Brookhaven National Laboratory have developed a new machine-learning (ML) framework that can zero in on which steps of a multistep chemical conversion should be tweaked to improve productivity. The approach could help guide the design of catalysts — chemical “dealmakers” that speed up reactions.
Scientists at Argonne have used machine learning algorithms to predict how long a lithium-ion battery will last.
Artificial intelligence systems can be trained to write human-like product reviews that assist consumers, marketers and professional reviewers, according to a study from Dartmouth College, Dartmouth’s Tuck School of Business, and Indiana University.
A team of researchers from the Department of Veterans Affairs, Oak Ridge National Laboratory, Harvard’s T.H. Chan School of Public Health, Harvard Medical School and Brigham and Women’s Hospital has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
Spinal cord stimulation is a minimally invasive FDA-approved treatment to manage chronic pain such as back and neck pain. The ability to accurately predict which patients will benefit from this treatment in the long term is unclear and currently relies on the subjective experience of the implanting physician. A study is the first to use machine-learning algorithms in the neuromodulation field to predict long-term patient response to spinal cord stimulation.
A team of researchers led by the University of Minnesota has significantly improved the performance of numerical predictions for agricultural nitrous oxide emissions. The first-of-its-kind knowledge-guided machine learning model is 1,000 times faster than current systems and could significantly reduce greenhouse gas emissions from agriculture.
In an article published today in Scientific Reports (https://www.nature.com/articles/s41598-022-10466-8), researchers from Bar-Ilan University in Israel reveal that the brain learns completely differently than has been assumed since the 20th century. The new experimental observations suggest that learning is mainly performed in neuronal dendritic trees, where the trunk and branches of the tree modify their strength, as opposed to modifying solely the strength of the synapses (dendritic leaves), as was previously thought. These observations also indicate that the neuron is actually a much more complex, dynamic and computational element than a binary element that can fire or not. Just one single neuron can realize deep learning algorithms, which previously required an artificial complex network consisting of thousands of connected neurons and synapses. The new demonstration of efficient learning on dendritic trees calls for new approaches in brain research, as well as for the generation
Experts from the University of Nottingham have developed a ground-breaking software, which combines DNA sequencing and machine learning to help them find where, and to what extent, antibiotic resistant bacteria is being transmitted between humans, animals and the environment.
Matt Ajemian, Ph.D., has received a $1,103,081 NSF CAREER grant for a project that will build fundamental knowledge on where and when large shell-crushing predators feed in order to ensure a sustainable future for shellfish species. Further, the work can provide guidance to shellfish restoration programs that are currently “flying blind” with respect to predation risk.
Proteins are the molecular machinery that makes life possible, and researchers have long been interested in a key trait of protein function: their three-dimensional structure. A new study by Georgia Tech and Oak Ridge National Laboratory details a computational tool able to predict the structure protein complexes – and lends new insights into the biomolecular mechanisms of their function.
To engineer proteins for specific functions, scientists change a protein sequence and experimentally test how that change alters its function. Because there are too many possible amino acid sequence changes to test them all in the laboratory, researchers build computational models that predict protein function based on amino acid sequences. Scientists have now combined multiple machine learning approaches for building a simple predictive model that often works better than established, complex methods.
Artificial intelligence advances how scientists explore materials. Researchers from Ames Laboratory and Texas A&M University trained a machine-learning (ML) model to assess the stability of rare-earth compounds. The framework they developed builds on current state-of-the-art methods for experimenting with compounds and understanding chemical instabilities.
Scientists have devised a way to engineer yeast to produce sustainable, eco-friendly commodity chemicals using computing power as a guide.
Researchers at Los Alamos National Laboratory are applying machine learning algorithms to help interpret massive amounts of ground deformation data collected with Interferometric Synthetic Aperture Radar (InSAR) satellites; the new algorithms will improve earthquake detection.
A team including a Lawrence Livermore National Laboratory (LLNL) mathematician and collaborators at the University of Massachusetts, Dartmouth and the University of Mississippi, has developed a machine learning-based technique capable of automatically deriving the motion of binary black holes from raw gravitational wave data.
Researchers at Washington University in St. Louis have recently developed a successful predictive model for hospitalized cancer patients that integrates heterogeneous data available in electronic health records.
A study is the first to reveal detailed behavior of massive goliath groupers. Until now, no studies have documented their fine-scale behavior. What is known about them has been learned from divers, underwater video footage, and observing them in captivity. Using a multi-sensor tag with a three axis accelerometer, gyroscope and magnetometer as well as a temperature, pressure and light sensor, a video camera and a hydrophone, researchers show how this species navigates through complex artificial reef environments, maintain themselves in high current areas, and how much time they spend in different cracks and crevices – none of which would be possible without the tag.
Certain biological events, such as proteins changing their shapes to perform some functions, occur so quickly that current methods of molecular imaging cannot capture them. Now, a research team has created a machine-learning technique that can “fill in” missing data needed to document proteins in action in time scales of a few quadrillionths of a second.
Deep brain stimulation (DBS) has been demonstrated to be an effective treatment for many patients suffering with treatment-resistant depression, but exactly how it works is not known.
Argonne scientists have received two high-profile grants from the U.S. Department of Energy that will help scientists at the U.S. National Laboratories take advantage of the latest developments in machine learning technology.
Experts at the U.S. Department of Energy’s Ames Laboratory and their collaborators have identified the way to tune the strength and ductility of a class of materials called high-entropy alloys. The discovery may help power-generation and aviation industry develop more efficient engines.
The Arkansas Economic Development Commission, using flow-through funding from the National Science Foundation, has awarded a postdoctoral research fellow at UA Little Rock a grant worth more than $40,000 to create a machine learning model to predict refugee counts in the United States.
New AI model called EVE, developed by scientists at Harvard Medical School and Oxford University, outperforms other AI methods in determining whether a gene variant is benign or disease-causing.
When applied to more than 36 million variants across 3,219 disease-associated proteins and genes, EVE indicated more than 256,000 human gene variants of unknown significance that should be reclassified as benign or pathogenic.
John F. McDonald and his research team have created a ‘multi-algorithm’ machine learning approach to boost accuracy in predicting drug responses for ovarian cancer patients.
Researchers reporting in ACS Nano have developed a dynamic respirator that modulates its pore size in response to changing conditions, such as exercise or air pollution levels, allowing the wearer to breathe easier when the highest levels of filtration are not required.
Chuck Stewart, an expert in the ecological applications of computer vision, is part of the newly created Imageomics Institute, founded with a $15 million grant from the National Science Foundation to use images of living organisms to understand biological processes.
A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows.
The NSF grant will enable scientists to elucidate trait evolution across species using statistical and supervised machine learning approaches to vigorously and accurately predict general and specific evolutionary mechanisms that also will be applicable to various genomic and transcriptomic data for evolutionary discovery.
Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis.
Argonne and Parallel Works, Inc., won the Federal Laboratory Consortium’s Midwest Regional Award for Excellence in Technology Transfer for bringing Argonne’s Machine Learning-Genetic Algorithm (ML-GA) design optimization software to commercialization.
Argonne recently teamed up with a Colorado-based biofuel company to perform a critical lifecycle analysis of its Next Gen technology to produce renewable jet fuel from corn grain in what could be a game-changer in biofuel industry.
Addenbrooke’s Hospital in Cambridge along with 20 other hospitals from across the world and healthcare technology leader, NVIDIA, have used artificial intelligence (AI) to predict Covid patients’ oxygen needs on a global scale.
The U.S. Department of Energy (DOE) announced $1 million for a one-year collaborative research project to develop artificial intelligence (AI) and machine learning (ML) algorithms for biomedical, personal healthcare, or other privacy-sensitive datasets.
One of the frequently used methods to monitor endangered whales is called passive acoustics technology, which doesn’t always perform well.