Traditionally, we've learned about nature through observation. Think of Johannes Kepler poring over Tycho Brahe's tables of planetary positions and trying to discern the underlying pattern. Science has also advanced through simulation. An astronomer might model the movement of the Milky Way and its neighboring galaxy, Andromeda, and predict that they'll collide in a few billion years. Both observation and simulation help scientists generate hypotheses that can then be tested with further observations. Generative modeling differs from both of these approaches.

In our efforts to understand the Universe, we're getting greedy, making more observations than we know what to do with. Satellites beam down hundreds of terabytes of information each year, and one telescope under construction in Chile will produce 15 terabytes of pictures of space every night. It's impossible for humans to sift through it all. As astronomer Carlo Enrico Petrillo told The Verge: "Looking at images of galaxies is the most romantic part of our job. The problem is staying focused."

No human, or team of humans, could possibly keep up with the avalanche of information produced by many of today's physics and astronomy experiments. Some of them record terabytes of data every day--and the torrent is only increasing. The Square Kilometer Array, a radio telescope slated to switch on in the mid-2020s, will generate about as much data traffic each year as the entire internet. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. The deluge has many scientists turning to artificial intelligence for help. With minimal human input, AI systems such as artificial neural networks--computer-simulated networks of neurons that mimic the function of brains--can plow through mountains of data, highlighting anomalies and detecting patterns that humans could never have spotted.

No human, or team of humans, could possibly keep up with the avalanche of information produced by many of today's physics and astronomy experiments. Some of them record terabytes of data every day -- and the torrent is only increasing. The Square Kilometer Array, a radio telescope slated to switch on in the mid-2020s, will generate about as much data traffic each year as the entire internet. The deluge has many scientists turning to artificial intelligence for help. With minimal human input, AI systems such as artificial neural networks -- computer-simulated networks of neurons that mimic the function of brains -- can plow through mountains of data, highlighting anomalies and detecting patterns that humans could never have spotted.

Kevin Schawinski had a problem. In 2007 he was an astrophysicist at Oxford University and hard at work reviewing seven years' worth of photographs from the Sloan Digital Sky Survey--images of more than 900,000 galaxies. He spent his days looking at image after image, noting whether a galaxy looked spiral or elliptical, or logging which way it seemed to be spinning. Technological advancements had sped up scientists' ability to collect information, but scientists were still processing information at the same rate. After working on the task full time and barely making a dent, Schawinski and colleague Chris Lintott decided there had to be a better way to do this.

In our efforts to understand the Universe, we're getting greedy, making more observations than we know what to do with. Satellites beam down hundreds of terabytes of information each year, and one telescope under construction in Chile will produce 15 terabytes of pictures of space every night. It's impossible for humans to sift through it all. As astronomer Carlo Enrico Petrillo told The Verge: "Looking at images of galaxies is the most romantic part of our job. The problem is staying focused."

Discovering the secrets of the universe is not a task for the timid and the impatient; there's a need to peer into the deepest reaches of outer space and try to make sense of distant galaxies, stars, gas clouds, quasars, halos, and black holes. "Understanding how these objects behave and how they interact gives us answers to how the universe was formed and how it works," says Kevin Schawinski, an astrophysicist and assistant professor in the Institute for Astronomy at ETH Zurich, the Swiss Federal Institute of Technology. The problem is that traditional tools such as telescopes can see only so far, even with radical advances in optics and the placement of observatories in space, where they are free of the light and dust of Earth. For instance, the Hubble Telescope changed the way astrophysicists and astronomers viewed deep space by delivering far clearer images than previously possible. Of course, in this context, distance and time are inextricably linked.

Since the late 1980s particle physicists have used AI even as the concept of a neural network was barely in the public's consciousness. AI and particle physics go hand in hand as the experiments the physicists perform usually revolves around seeking out patterns in the data from particle detectors and AI is excellent at pattern detection. Boaz Klima, a Physicists from the Fermi National Accelerator Laboratory, also called Fermilab, says "It took us several years to convince people that this is not just some magic, hocus-pocus, black box stuff." He was amongst the first to adopt AI tools but today, it's a part of standard particle physics practices. Usually, particle physicists aim to comprehend the way the inner gears of the universe works, typically by colliding subatomic particles at hit speeds to break them down into even smaller and more unusual kinds of matter.

Particle physicists began fiddling with artificial intelligence (AI) in the late 1980s, just as the term "neural network" captured the public's imagination. Their field lends itself to AI and machine-learning algorithms because nearly every experiment centers on finding subtle spatial patterns in the countless, similar readouts of complex particle detectors--just the sort of thing at which AI excels. "It took us several years to convince people that this is not just some magic, hocus-pocus, black box stuff," says Boaz Klima, of Fermi National Accelerator Laboratory (Fermilab) in Batavia, Illinois, one of the first physicists to embrace the techniques. Neural networks search for fingerprints of new particles in the debris of collisions at the LHC. Particle physicists strive to understand the inner workings of the universe by smashing subatomic particles together with enormous energies to blast out exotic new bits of matter.

Astronomer Kevin Schawinski has spent much of his career studying how massive black holes shape galaxies. But he isn't into dirty work--dealing with messy data--so he decided to figure out how neural networks could do it for him. Problem is, he and his cosmic colleagues suck at that sophisticated kind of coding. That changed when another professor at Schawinski's institution, ETH Zurich, sent him an email and CCed Ce Zhang, who actually is a computer scientist. "You guys should talk," the email said.