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    Machine learning tagged posts

    Hey Siri: How much does this Galaxy Cluster Weigh?

    July 20, 2022, Categories  Astronomy/Space

    It’s been nearly a century since astronomer Fritz Zwicky first calculated the mass of the Coma Cluster, a dense collection of almost 1,000 galaxies located in the nearby universe. But estimating the mass of something so huge and dense, not to mention 320 million light-years away, has its share of problems—then and now. Zwicky’s initial measurements, and the many made since, are plagued by sources of error that bias the mass higher or lower.

    Now, using tools from machine learning, a team led by Carnegie Mellon University physicists has developed a deeplearning method that accurately estimates the mass of the Coma Cluster and effectively mitigates the sources of error.

    “People have made mass estimates of the Coma Cluster for many, many years...

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    AI reveals unsuspected Math underlying Search for Exoplanets

    May 25, 2022, Categories  Physics, Astronomy/Space

    chart explaining gravitational microlensing
    This infographic explains the light curve astronomers detect when viewing a microlensing event, and the signature of an exoplanet: an additional uptick in brightness when the exoplanet lenses the background star. (Image Credit: NASA / ESA / K. Sahu / STScI)

    Artificial intelligence (AI) algorithms trained on real astronomical observations now outperform astronomers in sifting through massive amounts of data to find new exploding stars, identify new types of galaxies and detect the mergers of massive stars, accelerating the rate of new discovery in the world’s oldest science.

    But AI, also called machine learning, can reveal something deeper, University of California, Berkeley, astronomers found: Unsuspected connections hidden in the complex mathematics arising from general relativity—...

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    Harnessing Noise in Optical Computing for AI

    January 26, 2022, Categories  Physics, Technology/Electronics

    An illustration of the UW ECE-led research team’s integrated optical computing chip and “handwritten” numbers it generated. The chip contains an artificial neural network that can learn how to write like a human in its own, distinct style. This optical computing system uses “noise” (stray photons from lasers and thermal background radiation) to augment its creative capabilities. The system is also approximately 10 times faster than comparable conventional digital computers and more energy efficient, helping to put AI and machine learning on a path toward environmental sustainability. Illustration by Changming Wu

    A research team has developed an optical computing system for AI and machine learning that not only mitigates the noise inherent to optical computing but actually use...

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    Machine Learning for Morphable Materials

    January 15, 2022, Categories  Health/Medical, Biology/Biotechnology

    New platform can program the transformation of 2D stretchable surfaces into specific 3D shapes. Flat materials that can morph into three-dimensional shapes have potential applications in architecture, medicine, robotics, space travel, and much more. But programming these shape changes requires complex and time-consuming computations.

    Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a platform that uses machine learning to program the transformation of 2D stretchable surfaces into specific 3D shapes.

    “While machine learning methods have been classically employed for image recognition and language processing, they have also recently emerged as powerful tools to solve mechanics problems,” said Katia Bertoldi, the Wil...

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