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Arthur T Knackerbracket has found the following story:

The perennial optimists at the Search for Extraterrestrial Intelligence, or SETI, have joined the rest of the world in deploying AI to help manage huge data sets — and their efforts almost instantly bore fruit. Seventy-two new "fast radio bursts" from a mysteriously noisy galaxy 3 billion miles [light years] away were discovered in previously analyzed data by using a custom machine learning model.

To be clear, this isn't Morse code or encrypted instructions to build a teleporter, à la Contact, or at least not that we know of. But these fast radio bursts, or FRBs, are poorly understood and may very well represent, at the very least, some hitherto unobserved cosmic phenomenon. FRB 121102 is the only stellar object known to give off the signals regularly, and so is the target of continued observation.

The data comes from the Green Bank Telescope in West Virginia (above), which was pointed toward this source of fast and bright (hence the name) bursts for five hours in August of 2017. Believe it or not, that five-hour session yielded 400 terabytes of transmission data.

Initial "standard" algorithms identified 21 FRBs, all happening in one hour's worth of the observations. But Gerry Zhang, a graduate student at UC Berkeley and part of the Breakthrough Listen project, created a convolutional neural network system that would theoretically scour the data set more effectively. Sure enough, the machine learning model picked out 72 more FRBs in the same period.

That's quite an improvement, though it's worth noting that without manual and traditional methods to find an initial set of interesting data, we would have little with which to train such neural networks. They're complementary tools; one is not necessarily succeeding the other.

-- submitted from IRC

Original Submission