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Machine-Learning Algorithm Predicts Laboratory Earthquakes
MIT Technology Review reported a breakthrough that raises the possibility that real earthquake prediction could be on the horizon. The team is cautious about the new technique’s utility for real earthquakes, but the work opens up new avenues of research in this area.
Bertrand Rouet-Leduc at Los Alamos National Laboratory led a team that trained a machine-learning algorithm to spot the tell-tale signs in a laboratory earthquake simulator. Using recorded acoustic emissions from experimental system that follows the Gutenberg-Richter distribution were fed into a ML algorithm.
To their astonishment, the algorithm gave accurate predictions even when an earthquake’s probability was not imminent under existing models. “We show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy,”.
From TechCrunch: sources are reporting that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague, but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow.
From the March 2nd 2017 Data Science Weekly – Issue 171 Editor’s Pick
On the Origin of Deep Learning
This paper reviews the evolutionary history of deep learning models. It covers the genesis of neural networks when associationism modeling of the brain is studied, through to the models that dominate the last decade of research in deep learning and extends to recent popular models like variational autoencoder and generative adversarial nets:
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