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Astronomy cast episode 637 machine learning in astronomy. Welcome to astronomy cast from weekly facts based journey through the cosmos

where you help you understand not only what we know but how we know what we know. I'm Fr. Kaine publisher of university and with me as always Dr. Pamela Gay, a senior scientist for the planetary science institute and the director of cosmocost. How are you doing?

I am doing well. I have given in to Presbyopia and I have gotten glasses and as someone put it the trees they now have leaves.

I'm ready. It's my time as well. I think I can you know there's only so far my arms will let me stretch my phone away from my face. Yeah, getting old socks.

Presbyopia is such a cool word. So if you're going to have something go wrong, may it at least have a really cool word to describe it.

Right. Yeah, if only all medical and I guess age conditions were like that. Yeah. Computers are a big part of astronomy but mostly they've been relegated to doing calculations.

But recent developments in machine learning have changed everything giving computers the ability to do jobs that humans could only do in the past.

I guess it's not that surprising that computers machine learning specifically are starting to show up across the field of astronomy.

Computers have been tied in so closely to everything that astronomers do that it's just a matter of time before the I guess the artificial intelligence shows up to really run things.

Well, and it's getting to the point where the rate at which we're acquiring data is growing exponentially. Yeah.

And the number of astronomers in the field is not growing. And so since we don't have more astronomers to deal with all of the data, we've really got three choices.

Students again, not enough of them. The general public and we do totally use the general public goes into one of our episodes on citizen science.

But again, not enough of them because exponential growth. And this is where we have to turn to computers and help the processing speeds continue to grow at a rate that allows us to keep up with that flux of data.

Now, it's funny. You are uniquely positioned to talk about this situation because you have been at the forefront of citizen science of identifying things that computers can't do that humans can do and are happy to do or will begrudgingly do if you let them know that the science is important.

And so those rocks really do need to be mapped. But just this idea that there's things that computers are really bad at. And so let's organize a whole bunch of human beings to do the things that humans can still do that computers can't do.

Is that landscape starting to change from your perspective?

It's changing in terms of how we are interfacing with volunteers. Once upon a time back in the days that the Galaxy Z Project entered the battlefield of human versus data set, there was an issue where the Slunders of Skyser V had so many tens of thousands of hundreds of thousands of galaxies in it that

there was no way poor Kevin Schvinsky was going to as part of his dissertation get through hand marking all of the galaxies without madness occurring.

It was a job for grad students, but there weren't enough of those. And at that point in time, software was nowhere near being capable of being trained to mark galaxies.

This was about the point that one of my colleagues when she was working under dissertation back in the days of my space of life journal posted something along the lines of my software has discovered that wherever there are lizards, there are sailboats except sometimes their mountains.

Because the training set for the software turned out where they were marking lizard lizard lizard, the training set, all the triangles and the training set were sailboats except when they got to other data sets, there were triangles that were mountains.

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