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.

Of course, the use of computers to aid in scientific research goes back about 75 years, and the method of manually poring over data in search of meaningful patterns originated millennia earlier. But some scientists are arguing that the latest techniques in machine learning and AI represent a fundamentally new way of doing science. One such approach, known as generative modeling, can help identify the most plausible theory among competing explanations for observational data, based solely on the data, and, importantly, without any preprogrammed knowledge of what physical processes might be at work in the system under study. Proponents of generative modeling see it as novel enough to be considered a potential “third way” of learning about the universe.

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. (He eventually deduced that planets move in elliptical orbits.) 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.

“It’s basically a third approach, between observation and simulation,” says Kevin Schawinski, an astrophysicist and one of generative modeling’s most enthusiastic proponents, who worked until recently at the Swiss Federal Institute of Technology in Zurich (ETH Zurich). “It’s a different way to attack a problem.”

Some scientists see generative modeling and other new techniques simply as power tools for doing traditional science. But most agree that AI is having an enormous impact, and that its role in science will only grow. Brian Nord, an astrophysicist at Fermi National Accelerator Laboratory who uses artificial neural networks to study the cosmos, is among those who fear there’s nothing a human scientist does that will be impossible to automate. “It’s a bit of a chilling thought,” he said.

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Discovery by Generation

Ever since graduate school, Schawinski has been making a name for himself in data-driven science. While working on his doctorate, he faced the task of classifying thousands of galaxies based on their appearance. Because no readily available software existed for the job, he decided to crowdsource it — and so the Galaxy Zoo citizen science project was born. Beginning in 2007, ordinary computer users helped astronomers by logging their best guesses as to which galaxy belonged in which category, with majority rule typically leading to correct classifications. The project was a success, but, as Schawinski notes, AI has made it obsolete: “Today, a talented scientist with a background in machine learning and access to cloud computing could do the whole thing in an afternoon.”

Schawinski turned to the powerful new tool of generative modeling in 2016. Essentially, generative modeling asks how likely it is, given condition X, that you’ll observe outcome Y. The approach has proved incredibly potent and versatile. As an example, suppose you feed a generative model a set of images of human faces, with each face labeled with the person’s age. As the computer program combs through these “training data,” it begins to draw a connection between older faces and an increased likelihood of wrinkles. Eventually it can “age” any face that it’s given — that is, it can predict what physical changes a given face of any age is likely to undergo.

The best-known generative modeling systems are “generative adversarial networks” (GANs). After adequate exposure to training data, a GAN can repair images that have damaged or missing pixels, or they can make blurry photographs sharp. They learn to infer the missing information by means of a competition (hence the term “adversarial”): One part of the network, known as the generator, generates fake data, while a second part, the discriminator, tries to distinguish fake data from real data. As the program runs, both halves get progressively better. You may have seen some of the hyper-realistic, GAN-produced “faces” that have circulated recently — images of “freakishly realistic people who don’t actually exist,” as one headline put it.

More broadly, generative modeling takes sets of data (typically images, but not always) and breaks each of them down into a set of basic, abstract building blocks — scientists refer to this as the data’s “latent space.” The algorithm manipulates elements of the latent space to see how this affects the original data, and this helps uncover physical processes that are at work in the system.

The idea of a latent space is abstract and hard to visualize, but as a rough analogy, think of what your brain might be doing when you try to determine the gender of a human face. Perhaps you notice hairstyle, nose shape, and so on, as well as patterns you can’t easily put into words. The computer program is similarly looking for salient features among data: Though it has no idea what a mustache is or what gender is, if it’s been trained on data sets in which some images are tagged “man” or “woman,” and in which some have a “mustache” tag, it will quickly deduce a connection.

In a paper published in December in Astronomy & Astrophysics, Schawinski and his ETH Zurich colleagues Dennis Turp and Ce Zhang used generative modeling to investigate the physical changes that galaxies undergo as they evolve. (The software they used treats the latent space somewhat differently from the way a generative adversarial network treats it, so it is not technically a GAN, though similar.) Their model created artificial data sets as a way of testing hypotheses about physical processes. They asked, for instance, how the “quenching” of star formation — a sharp reduction in formation rates — is related to the increasing density of a galaxy’s environment.

For Schawinski, the key question is how much information about stellar and galactic processes could be teased out of the data alone. “Let’s erase everything we know about astrophysics,” he said. “To what degree could we rediscover that knowledge, just using the data itself?”

First, the galaxy images were reduced to their latent space; then, Schawinski could tweak one element of that space in a way that corresponded to a particular change in the galaxy’s environment — the density of its surroundings, for example. Then he could re-generate the galaxy and see what differences turned up. “So now I have a hypothesis-generation machine,” he explained. “I can take a whole bunch of galaxies that are originally in a low-density environment and make them look like they’re in a high-density environment, by this process.”  Schawinski, Turp and Zhang saw that, as galaxies go from low- to high-density environments, they become redder in color, and their stars become more centrally concentrated. This matches existing observations about galaxies, Schawinski said. The question is why this is so.

The next step, Schawinski says, has not yet been automated: “I have to come in as a human, and say, ‘OK, what kind of physics could explain this effect?’” For the process in question, there are two plausible explanations: Perhaps galaxies become redder in high-density environments because they contain more dust, or perhaps they become redder because of a decline in star formation (in other words, their stars tend to be older). With a generative model, both ideas can be put to the test: Elements in the latent space related to dustiness and star formation rates are changed to see how this affects galaxies’ color. “And the answer is clear,” Schawinski said. Redder galaxies are “where the star formation had dropped, not the ones where the dust changed. So, we should favor that explanation.”

The approach is related to traditional simulation, but with critical differences. A simulation is “essentially assumption-driven,” Schawinski said. “The approach is to say, ‘I think I know what the underlying physical laws are that give rise to everything that I see in the system.’ So, I have a recipe for star formation, I have a recipe for how dark matter behaves, and so on. I put all of my hypotheses in there, and I let the simulation run. And then I ask: Does that look like reality?” What he’s done with generative modeling, he said, is “in some sense, exactly the opposite of a simulation. We don’t know anything; we don’t want to assume anything. We want the data itself to tell us what might be going on.”

The apparent success of generative modeling in a study like this obviously doesn’t mean that astronomers and graduate students have been made redundant — but it appears to represent a shift in the degree to which learning about astrophysical objects and processes can be achieved by an artificial system that has little more at its electronic fingertips than a vast pool of data. “It’s not fully automated science — but it demonstrates that we’re capable of at least in part building the tools that make the process of science automatic,” Schawinski said.

Generative modeling is clearly powerful, but whether it truly represents a new approach to science is open to debate. For David Hogg, a cosmologist at New York University and the Flatiron Institute (which, like Quanta, is funded by the Simons Foundation), the technique is impressive but ultimately just a very sophisticated way of extracting patterns from data — which is what astronomers have been doing for centuries. In other words, it’s an advanced form of observation plus analysis. Hogg’s own work, like Schawinski’s, leans heavily on AI; he’s been using neural networks to classify stars according to their spectra and to infer other physical attributes of stars using data-driven models. But he sees his work, as well as Schawinski’s, as tried-and-true science. “I don’t think it’s a third way,” he said recently. “I just think we as a community are becoming far more sophisticated about how we use the data. In particular, we are getting much better at comparing data to data. But in my view, my work is still squarely in the observational mode.”

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