news, journals and articles from all over the world.
There’s a fairly large flaw in the way that programmers are currently addressing ethical concerns related to artificial intelligence and autonomous vehicles (AVs). Namely, existing approaches don’t account for the fact that people might try to use the AVs to do something bad.
Read moreResearchers at Binghamton University, State University of New York are using machine learning to track the coronavirus and predict where it might surge next.
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Computer scientists, software developers and system administrators are coming together under one roof in the newly established Computational Sciences and Technology Division at the Department of Energy’s Thomas Jefferson National Accelerator Facility. Amber Boehnlein, Jefferson Lab’s chief information officer, has been promoted to associate director for computational sciences and technology, heading up the new division.
Read moreA new artificial intelligence system allowing shoppers on Facebook to identify characteristics of items in uploaded photographs is based on Cornell University computer vision research into fine-grained visual recognition.
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Understanding the source and network of signals as the brain functions is a central goal of brain research. Now, Carnegie Mellon engineers have created a system for high-density EEG imaging of the origin and path of normal and abnormal brain signals.
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When it fires, a neuron consumes significantly more energy than an equivalent computer operation. And yet, a network of coupled neurons can continuously learn, sense and perform complex tasks at energy levels that are currently unattainable for even state-of-the-art processors.What does a neuron do to save energy that a contemporary computer processing unit doesn’t?Computer modelling by researchers at Washington University in St.
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Researchers have developed a technology called “Artificial Chemist,” which incorporates artificial intelligence and an automated system for performing chemical reactions to accelerate R&D and manufacturing of commercially desirable materials.
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In a recent preprint (available through Cornell University’s open access website arXiv), a team led by a Lawrence Livermore National Laboratory computer scientist proposes a novel deep learning approach aimed at improving the reliability of classifier models designed for predicting disease types from diagnostic images, with an additional goal of enabling interpretability by a medical expert without sacrificing accuracy. The approach uses a concept called confidence calibration, which systematically adjusts the model’s predictions to match the human expert’s expectations in the real world.
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ISPOR concluded its Virtual ISPOR 2020 conference yesterday—its first completely virtual conference. The conference was redesigned as an online event when the COVID-19 pandemic required a necessary cancellation of the in-person conference.
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ISPOR held its second Virtual ISPOR 2020 plenary session this afternoon, “Health Economics and Outcomes Research and Clinical Decision Making—Advancing Meaningful Progress.”
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