Researchers at Breakthrough Listen, a SETI project led by the University of California, Berkeley, have now used machine learning to discover 72 new fast radio bursts from a mysterious source some 3 billion light years from Earth.
“This work is exciting not just because it helps us understand the dynamic behavior of fast radio bursts in more detail, but also because of the promise it shows for using machine learning to detect signals missed by classical algorithms,” said Andrew Siemion, director of the Berkeley SETI Research Centre and principal investigator for Breakthrough Listen, the initiative to find signs of intelligent life in the universe.
The AI algorithms dredged up the radio signals from data were recorded over a five-hour period on Aug. 26, 2017, by the Green Bank Telescope in West Virginia. An earlier analysis of the 400 TeraBytes of data employed standard computer algorithms to identify 21 bursts during that period. All were seen within one hour, suggesting that the source alternates between periods of quiescence and frenzied activity, said Berkeley SETI postdoctoral researcher Vishal Gajjar.
UC Berkeley Ph.D. student Gerry Zhang and collaborators subsequently developed a new, powerful machine-learning algorithm and reanalysed the 2017 data, finding an additional 72 bursts not detected originally. This brings the total number of detected bursts from FRB 121102 to around 300 since it was discovered in 2012.
Zhang’s team used some of the same techniques that internet technology companies use to optimize search results and classify images. They trained an algorithm known as a convolutional neural network to recognize bursts found by the classical search method used by Gajjar and collaborators, and then set it loose on the dataset to find bursts that the classical approach missed.
https://arxiv.org/pdf/1809.03043.pdf
http://news.berkeley.edu/2018/09/10/ai-helps-track-down-mysterious-cosmic-radio-bursts/
See, this is the sort of task for which AI is the only practical solution. Well, it is possible to put people in the loops, MTurk and the like - but nothing like this sort of problem and solution. It's really nice to run across an AI story which isn't complete bollox.
ReplyDeleteExcept that AI currently inherits the prejudice of the programmer.
ReplyDeleteMeaning that it's little wonder programmers looking for extra-terrestrial intelligence will find it using AI.
Simon B I question that assertion: in other sorts of artificial intelligence, you are certainly right; a bad training regimen will reflect the prejudice of the trainer.
ReplyDeleteBut with a ConvoNN, it's somewhat different...
machinelearninguru.com - Train a Convolutional Neural Network as a Classifier