
Astronomers explore uses for AI-generated images - timthorn
http://www.nature.com/news/astronomers-explore-uses-for-ai-generated-images-1.21398
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autocorr
A number of statistical and machine learning techniques have "gone mainstream"
in astronomical research right now, so it has made a very exciting time. There
is even a good textbook out now on the subject, _Statistics, Data Mining, and
Machine Learning in Astronomy: A Practical Python Guide for the Analysis of
Survey Data_ by Ivezic that we used for a graduate seminar series. This mostly
focuses on Bayesian inference and simple techniques like clustering
algorithms, support vector machines, etc. and not super fancy "deep
convolutional neural networks" like I see on HN most days :)

A funny anecdote makes me wonder actually if there have been barriers to
uptake in astronomy. You would think huge datasets and low manpower would be
the perfect recipe for machine learning! Well, historically cheap labor could
solve this by throwing undergrads at this. I actually even categorized ~10,000
maps of interstellar clouds by eye for one project (it took about a week if
you don't count the time to write the GUI). The anecdote though was that one
of the other grad students in the department was telling me how he was working
on applying machine learning techniques to classifying galaxy morphological
types. They had somewhere around 100,000 galaxies to type from their survey.
But, they first applied to the citizen science organization Galaxy Zoo, and
were featured as a project. They submitted classification instructions and the
data, and it was completed in... less than 24 hours. He told me afterwards
that, with how easy and fast it was, why bother spending weeks writing and
tuning some neural network yourself?

This made my wonder whether if Galaxy Zoo and other citizen science projects
are such good resources, would it stymie the need for incorporating machine
learning techniques into most projects? There are huge projects like the SKA
(mentioned in the article) and LSST that will give torrents of data. Those
might be too large for citizen science, and there are advantages to objective
statistical methods to begin with.

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GFK_of_xmaspast
Galaxy Zoo makes labeled training data which is always good.

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amelius
Interesting article.

However, it leaves me with the question why this process should converge to a
desired network. I mean, why doesn't this process generate training artifacts,
that get multiplied as the interaction between discriminator and generator
progresses?

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exit
maybe the complex distribution of "looks like a galaxy" images just isn't that
overwhelmingly complex, relative to how cheap computation & storage is
nowadays.

what surprises me is how these techniques can structure the space of
compressed representations in interesting ways. look at this discussion of
thought vectors: [https://www.technologyreview.com/s/541356/king-man-woman-
que...](https://www.technologyreview.com/s/541356/king-man-woman-queen-the-
marvelous-mathematics-of-computational-linguistics/)

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officialvke
Although I don't quite understand how this works, the results are fascinating
enough to convince me to to study machine learning to achieve the same
results.

As someone without formal STEM background, stepping in the path of machine
learning is very daunting.

If someone could recommend me good (and free) resources to get started with
machine learning or explain to me the process they took (in layman terms), I'd
appreciate it.

