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UrduA multi-institution research team has developed an optical chip that can train machine learning hardware.
Tag: Machine Learning
A possible game changer for next generation microelectronics
Researchers have discovered new properties of tiny magnetic whirlpools called skyrmions. Their pivotal discovery could lead to a new generation of microelectronics for memory storage with vastly improved energy efficiency.
Argonne scientists promote FAIR standards for managing artificial intelligence models
Argonne computational scientists adopt new framework for making AI models more findable, accessible, interoperable and reusable.
Johns Hopkins APL Releases First-Ever Global Estimates for Road Transportation Greenhouse Emissions Leveraging Artificial Intelligence and Satellite Images
APL scientists have leveraged the global coverage of satellite imagery and the strengths of machine learning to create the first automated approach for estimating greenhouse gas emissions from the road transportation sector.
New technology to reduce potholes
Researchers have developed new “intelligent compaction” technology, which integrates into a road roller and can assess in real-time the quality of road base compaction.
Detecting Nuclear Threats with Artificial Reasoning
PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.
Scientists develop new algorithm that may provide insights into battery corrosion
New method provides insight into how materials evolve in real-time.
Deep learning underlies geographic dataset used in hurricane response
Over the past seven years, researchers in ORNL’s Geospatial Science and Human Security Division have mapped and characterized all structures within the United States and its territories to aid FEMA in its response to disasters.
New machine-learning simulations reduce energy need for mask fabrics, other materials
A new computational effort between Argonne and 3M promises to reduce energy consumption without sacrificing material quality in the production of nonwoven plastics, commonly used in surgical masks.
FAU Lands $1.2 Million NSF Grant to Transform Prosthetic Hand Control
Current prosthetic hands have five individually actuated digits, yet only one grasp function can be controlled at a time, which makes sophisticated tasks largely impossible.
AI model using daily step counts predicts unplanned hospitalizations during cancer therapy
An artificial intelligence (AI) model developed by researchers can predict the likelihood that a patient may have an unplanned hospitalization during their radiation treatments for cancer. The machine-learning model uses daily step counts as a proxy to monitor patients’ health as they go through cancer therapy, offering clinicians a real-time method to provide personalized care. Findings will be presented today at the American Society for Radiation Oncology (ASTRO) Annual Meeting.
Study finds that artificial intelligence can determine race from medical images
Researchers have found that AI models could accurately predict self-reported race in several types of medical images, suggesting that race information could be unknowingly incorporated into image analysis models.
5 big strides from Argonne towards nuclear energy’s future
Nuclear energy is an exciting carbon-free energy source. Recent work at Argonne National Laboratory shows how nuclear energy can improve and why it is such an enticing resource in the fight against climate change.
Machine Learning Takes Hold in Nuclear Physics
Scientists have begun turning to new tools offered by machine learning to help save time and money. In the past several years, nuclear physics has seen a flurry of machine learning projects come online, with many papers published on the subject. Now, 18 authors from 11 institutions summarize this explosion of artificial intelligence-aided work in “Machine Learning in Nuclear Physics,” a paper recently published in Reviews of Modern Physics.
AACN Research Grants Influence Nursing Practice
The American Association of Critical-Care Nurses announces the newest recipients of its annual research grants and invites clinicians and researchers to submit projects online by Oct. 28, 2022, for the next application cycle. AACN will award up to three $50,000 Impact Research Grants in 2023, as well as co-sponsoring the AACN-Sigma Critical Care Grant, with up to $10,000 in funding.
Common Approach to Demystify Black Box AI Not Ready for Prime Time, Study Finds
Clinical AI tools hold the promise to transform the practice of medicine, but lack of transparency in some tools is an ongoing challenge.
Coalition for Health AI Updates Progress and Plans to Issue Guidelines for the Responsible Use of AI in Healthcare
The coalition focused on the foundational themes of Bias, Equity, and Fairness in its first in a series of workshops aimed at developing guidelines for the responsible use of AI in healthcare.
Scientists use machine learning to accelerate materials discovery
Scientists at Argonne National Laboratory have recently demonstrated an automated process for identifying and exploring promising new materials by combining machine learning (ML) and high performance computing.
Helping companies improve energy efficiency through high performance computing
The U.S. Department of Energy (DOE) has awarded DOE’s Argonne National Laboratory with $600,000 in federal funding to work on two new projects that will advance cutting edge manufacturing and clean energy technologies.
Unreliable neurons improve brain functionalities
Neuronal silencing periods facilitate an advantageous mechanism for temporal sequence identification and demonstrate a useful new AI mechanism for ATM’s equipped with secure handwriting recognition.
Machine learning creates opportunity for new personalized therapies
Researchers at the University of Michigan Rogel Cancer Center have developed a computational platform that can predict new and specific metabolic targets in ovarian cancer, suggesting opportunities to develop personalized therapies for patients that are informed by the genetic makeup of their tumors. The study appeared in Nature Metabolism.
Mutational signature linking bladder cancer and tobacco smoking found with new AI tool
UC San Diego researchers have for the first time discovered a pattern of DNA mutations that links bladder cancer to tobacco smoking. The work could help researchers identify what environmental factors, such as exposure to tobacco smoke and UV radiation, cause cancer in certain patients. It could also lead to more customized treatments for a patient’s specific cancer.
Researchers Aim to Use AI to Predict Rare Diseases
Penn Medicine researchers will help lead development of an algorithm to flag patients at risk of rare disease thanks to a $4.7 million NIH grant
Mount Sinai Researchers Use Artificial Intelligence to Uncover the Cellular Origins of Alzheimer’s Disease and Other Cognitive Disorders
Deep learning models represent “an entirely new paradigm for studying dementia”
Researchers combine data science and machine learning techniques to improve traditional MRI image reconstruction
University of Minnesota Twin Cities researchers have found a way to improve the performance of traditional Magnetic Resonance Imaging (MRI) reconstruction techniques, allowing for faster MRIs without relying on the use of newer deep learning methods.
UC San Diego Joins NIH ‘Bridge to Artificial Intelligence’ Program
UC San Diego scientists will lead several components of Bridge2AI, a new NIH-funded program to promote the use of AI in health and research.
Breakthrough Reported in Machine Learning-Enhanced Quantum Chemistry
The equations of quantum mechanics require too much computer time and power when used to predict behavior in large systems. Researchers have now shown that machine learning models can mimic the basic structure from first principles, which can be very difficult to simulate directly. The result is predictions that are easy to compute and are accurate in a wide range of chemical systems.
Personalized prediction of depression treatment outcomes with wearables
An interdisciplinary team built a multitask machine learning model for randomized controlled trials of the efficacy of certain depression treatments on individuals.
The Effects of Copper, Patient-Relevant Tissue Models, and Artificial Intelligence Featured in Sept. 2022 Issue of ToxSci
A new ToxPoint articles argues that “Copper Is the New Showstopper,” while a commentary calls for patient-relevant tissue models in the September 2022 issue of Toxicological Sciences (ToxSci), the official journal of the Society of Toxicology (SOT). Other featured articles…
Low-Cost Disease Diagnosis by Mapping Heart Sounds
In the Journal of Applied Physics, researchers develop a method to identify aortic valve dysfunction using complex network analysis that is accurate, simple to use, and low-cost. They used heart sound data to create a complex network of connected points, which was split into sections, and each part was represented with a node. If the sound in two portions was similar, a line was drawn between them. In a healthy heart, the graph showed two distinct clusters of points, with many nodes unconnected. A heart with aortic stenosis contained many more correlations and edges.
Preventing Pressure Injuries Among ICU Patients With COVID-19 Requires Extra Vigilance
Patients who are critically ill with COVID-19 are at exceptionally high risk for developing healthcare-associated pressure injuries, especially those related to medical devices, and clinicians must consider additional factors beyond those assessed with common classification tools.
Lu brings the power of AI to the hospital
Chenyang Lu at the McKelvey School of Engineering is leading a charge to bring artificial intelligence into hospitals for the benefit of patients’ health — and doctors’ well-being.
Big data in the ER
Researchers at Osaka University use machine learning methods on a large dataset of trauma patients to determine the factors that correlate with survival, which may significantly improve triage and rapid treatment procedures.
Precision health perspectives
In February, UCI launched the Institute for Precision Health, a campus-wide, interdisciplinary endeavor that merges UCI’s powerhouse health sciences, engineering, machine learning, artificial intelligence, clinical genomics and data science capabilities. The objective is to identify, create and deliver the most effective health and wellness strategy for each individual person and, in doing so, confront the linked challenges of health equity and the high cost of care.
How artificial intelligence could lower nuclear energy costs
Argonne scientists are building artificial intelligence systems to streamline operations and maintenance at advanced nuclear reactors.
Rensselaer Researchers to Address Big Data Challenges
Dr. Yangyang Xu, assistant professor of mathematical sciences at Rensselaer Polytechnic Institute, has received a $250,000 grant from the National Science Foundation (NSF) to research challenges associated with distributed big data in machine learning.Machine learning algorithms allow computers to make decisions, predictions, and recommendations on the basis of input training data without being explicitly told what information to look for in the data.
Get More from Your Lunch Break with Bite-Size Science
Take a break for lunch and nourish your brain with the latest in scientific discussions, presented by experts at Jefferson Lab. The second season of the lab’s summer series, Bite-Size Science, is now underway. The Bite-Size Science lunchtime lecture series features half-hour, live-streamed presentations on lab-related science, engineering and technology topics and presented by leaders in their fields. The presentations are tailored to non-scientists and are brief, free, and feature a chat feature for Q&A with the presenters.
UCLA researchers use artificial intelligence tools to speed critical information on drug overdose deaths
Fast data processing of overdose deaths, which have increased in recent years, is crucial to developing a rapid public health response. But the system now in place lacks precision and takes months. To correct that, UCLA researchers have developed an automated process that reduces data collection to a few weeks.
Making House Calls Guided by AI
Handheld computer vision and machine learning tool for identifying surgical wound infections debuts in rural Rwanda, enabling crucial care for women recovering from c-section in their homes. Project named first-prize winner in NIH Technology Accelerator Challenge for Maternal Health.
Using artificial intelligence to control digital manufacturing
Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum.
Can an algorithm teach scientists to write better quantum computer programs?
A new research project, funded by an Department of Energy Early Career Research Program Award, will help quantum computer scientists write better programs that fail less often.
JMIR Biomedical Engineering | Using Machine Learning to Reduce Treatment Burden
JMIR Publications recently published “Reducing Treatment Burden Among People With Chronic Conditions Using Machine Learning: Viewpoint” in JMIR Biomedical Engineering which reported that the COVID-19 pandemic has illuminated multiple challenges within the health care system and is unique to those living with chronic conditions.
SLAC expands and centralizes computing infrastructure to prepare for data challenges of the future
A computing facility at the Department of Energy’s SLAC National Accelerator Laboratory is doubling in size, preparing the lab for new scientific endeavors that promise to revolutionize our understanding of the world from atomic to cosmic scales but also require handling unprecedented data streams.
Techcyte announces the release of their second generation AI for Human Parasites
Techcyte, a leading developer of AI-based image analysis solutions for the diagnostics industry, is proud to announce the release of their second-generation solution for Human Fecal Trichrome (HFT) for human parasites.
Machine Learning Paves Way for Smarter Particle Accelerators
Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world.
Firm managers may benefit from transparency in machine-learning algorithms
In today’s business world, machine-learning algorithms are increasingly being applied to decision-making processes, which affects employment, education, and access to credit. But firms usually keep algorithms secret, citing concerns over gaming by users that can harm the predictive power of algorithms.
Machine learning identifies gun purchasers at risk of suicide
A first-of-its-kind study from the Violence Prevention Research Program at UC Davis shows an algorithm can forecast the likelihood of firearm suicide using handgun purchasing data.
ClearBuds: First wireless earbuds that clear up calls using deep learning
University of Washington researchers created ClearBuds, earbuds that enhance the speaker’s voice and reduce background noise.
Global Expert Panel Identifies 5 Areas Where Machine Learning Could Enhance Health Economics and Outcomes Research
Value in Health, the official journal of ISPOR—the professional society for health economics and outcomes research, announced today the publication of new guidance for health economics and outcomes research and decision makers in the use of an important class of artificial intelligence techniques.
Designed to identify wildlife by sound, the BirdNET app opens new avenues for citizen science
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.