Researchers from Tokyo Metropolitan University have applied machine-learning techniques to achieve fast, accurate estimates of local geomagnetic fields using data taken at multiple observation points, potentially allowing detection of changes caused by earthquakes and tsunamis. A deep neural network (DNN) model was developed and trained using existing data; the result is a fast, efficient method for estimating magnetic fields for unprecedentedly early detection of natural disasters. This is vital for developing effective warning systems that might help reduce casualties and widespread damage.
UW engineers developed a new machine-learning system that can help anesthesiologists predict the likelihood that a patient will experience low blood oxygen levels during surgery. This condition, called hypoxemia, can lead to serious consequences, such as infections and abnormal heart behavior. The team's system also gives real-world explanations behind its predictions. The researchers estimate that it could improve the ability of anesthesiologists to prevent 2.4 million more hypoxemia cases in the United States every year.
International team of physicists explained anomalous low temperature behavior of 'dirty' superconductors. These materials possess various non-trivial properties which make them necessary for quantum computers with superconductive qubits. In a paper published in Nature Physics, scientists report how 'dirty' superconductors can violate the conventional theory of superconductivity. These results make it possible to engineer superconductive qubits that are perfectly isolated from the outer disturbances and thus can be fully used for quantum computing.
As artificial intelligence becomes more sophisticated, much of the public attention has focused on how successfully these technologies can compete against humans at chess and other strategy games. A philosopher from the University of Houston has taken a different approach, deconstructing the complex neural networks used in machine learning to shed light on how humans process abstract learning.
MIT researchers have now devised a way to help robots navigate environments more like humans do. Their novel motion-planning model lets robots determine how to reach a goal by exploring the environment, observing other agents, and exploiting what they've learned before in similar situations. A paper describing the model was presented at this week's IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
A machine learning system aims to determine if a news outlet is accurate or biased.
In an article published today in the journal Nature Communications, researchers from Carnegie Mellon University; the University of California, San Diego; and St. Petersburg State University in Russia describe a new means of searching vast repositories of compounds produced by microbes. By analyzing the mass spectra of the compounds, they were able to identify known compounds within the repository and eliminate them from further analysis, focusing instead on the unknown variants that might potentially be better or more efficient antibiotics, anticancer drugs or other pharmaceuticals.
By translating a key human physical skill, whole-body balance, into an equation, engineers at UT Austin used the numerical formula to program their robot Mercury.
New research sheds light on how people decide whether behavior is moral or immoral. The findings could serve as a framework for informing the development of artificial intelligence and other technologies.
Cash-strapped environmental regulators have a powerful and cheap new weapon. Machine learning methods could more than double the number of violations detected, according to Stanford researchers.