Authors: George Rajna
Luckily, particle physicists don't have to deal with all of that data all by themselves. They partner with a form of artificial intelligence called machine learning that learns how to do complex analyses on its own.  Today my IBM team and my colleagues at the UCSF Gartner lab reported in Nature Methods an innovative approach to generating datasets from non-experts and using them for training in machine learning.  Tired of writing your own boring code for new software? Finally, there's an AI that can do it for you.  Welcome to Move Mirror, where you move in front of your webcam.  Understanding how a robot will react under different conditions is essential to guaranteeing its safe operation.  Marculescu, along with ECE Ph.D. student Chieh Lo, has developed a machine learning algorithm—called MPLasso—that uses data to infer associations and interactions between microbes in the GI microbiome.  A team of researchers from the University of Muenster in Germany has now demonstrated that this combination is extremely well suited to planning chemical syntheses—so-called retrosyntheses—with unprecedented efficiency.  Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics.  have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time.  Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning.  Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. 
Comments: 39 Pages.
[v1] 2018-08-02 06:44:14
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