Sky and Telescope - December 2017 - 21

You probably use artiﬁcial intelligence a dozen times a
day without realizing it. If you've recently scrolled through
your Facebook feed, browsed Netﬂix videos, used iPhone's
Siri, checked a relatively spam-free inbox, or just Googled a
term, you've made use of complex computer algorithms that
can learn from experience.
"Artiﬁcial intelligence," though correct, may be too
fanciful a term for these processes - no computer today is
capable of general intelligence or autonomy. Still, what the
algorithms can do is possibly paradigm-shifting. Rather than
humans programming a computer explicitly, the algorithms
use data to construct their own mathematical models -
sometimes ones too complex for human understanding. This
implementation of artiﬁcial intelligence is known as machine
learning, and it can ﬁlter through vast amounts of data.
Given the data tsunami facing astronomy (S&T:
Sept. 2016, p. 14), it should come as no surprise that machine
learning has popped up in just about every part of the celestial realm over the past few years (see box on page 28). From
exoplanets and variable stars to cosmology, machine learning
is going to play an ever-larger role in the coming decade of
astronomical research.

p TO SEE CATS Computer scientists at Google taught a powerful neural network to recognize, among other things, cat faces. The algorithm
"learned" from 10 million 200×200-pixel images drawn from YouTube
videos. When asked to render a cat, the algorithm produced this convincing image.

CAT: LE E T A L. / PROCEEDINGS OF THE 29TH INTER NATIONAL CONFER ENCE ON MACHINE
LE AR NING 2012; NEURONS: ENOT POLOSK UN / ISTOCK / G E T T Y IM AG ES PLUS

Big Data, Big Opportunity
Machine learning isn't new - pioneers of the ﬁeld hail back
to the 1950s. But it was long discounted as impractical,
requiring too much computational power to implement. Lack
of data was also a problem: Like humans, machines build
their internal models based on copious observations.
It was the advent of Big Data, as well as signiﬁcant
advances in computing, that ﬁnally enabled machine learning to take off. In one famous example, Andrew Ng (Stanford University), who led the Google Brain project, drew from
10 million YouTube videos to teach an algorithm to recognize - what else? - cats. Fortunately, the playing ﬁeld isn't
limited to internet whims. In astronomy, Big Data abounds.
"We are very ﬁrmly in the era of what we call 'survey
astronomy,'" says Lucianne Walkowicz (Adler Planetarium).
The Sloan Digital Sky Survey (SDSS), which has imaged
a third of the sky, qualiﬁed as Big Data when it began in
2000, but new and upcoming projects are dwarﬁng it. The
standard-bearer is the Large Synoptic Survey Telescope
(LSST), scheduled to begin science operations in 2022. It will
generate the equivalent of the SDSS every night, monitoring 37 billion stars and galaxies in space and time to make a
decade-long movie of the southern sky.
There are others as well: The Dark Energy Survey started
charting hundreds of millions of galaxies in 2013; the Gaia
satellite began mapping 1 billion stars in the Milky Way in
2014; and the Zwicky Transient Facility, due to see ﬁrst light
in late 2017, will be scanning 3,750 square degrees every
hour. That's not to mention archival data from past surveys.
The bytes have become numerous enough that, even with
dozens of graduate students or thousands of citizen scientists, human eyes simply can't scan all the available data.

"People don't scale," notes Joshua Bloom (University of
California, Berkeley), founder of the startup Wise.io. "And
especially experts don't scale."
Brian Nord (Fermilab) encountered this problem of scale
when he joined the Dark Energy Survey (DES) team, where
20 scientists spent months poring over 250 square degrees'
worth of images. They were looking for the warped shapes
that mark strong gravitational lenses, where clusters or massive galaxies bend the light from background objects.
About a thousand such lenses are already known, but the
full, 5,000-square-degree DES offers the potential to triple
that number, and LSST might reveal an order of magnitude
more. A few of these lenses could magnify exceedingly rare -
and cosmologically invaluable - distant supernovae. For this
reason, the researchers made sure to look at everything in the
preliminary data set, not applying any ﬁlters that might have
eased the search, in order to avoid missing legitimate sources.
"It was a painful number of pixels to visually scan," Nord
says. "I saw this, and I thought, 'Oh my God. This is so painful, there has got to be a better way.'"

MORE THAN JUST BIG
To qualify as "big" in the world of computer science,
data need more than volume; they also need variety
and velocity. Big Data is any huge amount of data
that comes in a variety of formats (such as images,
spectra, and times-series data) and must be dealt
with in a timely manner.

s k y a n d t e l e s c o p e . c o m * D E C E M B E R 2 0 17

http://www.Wise.io http://www.skyandtelescope.com

Sky and Telescope - December 2017

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