Welcome to Quantum Magazine's podcast. Each episode we bring you stories about developments in science and mathematics. I'm Susan Vallett. 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 can plow through mountains of data,

highlighting anomalies and detecting patterns that humans could never have spotted. These systems include artificial neural networks,

computer-simulated networks of neurons that mimic the function of brains. Of course, the use of

computers to aid in scientific research goes back about 75 years, and the method of manually pouring 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 is known as generative modeling.

It can help identify the most plausible theory among competing explanations for observational data

based solely on the data and without any pre-programmed knowledge of what physical

processes might be at work in the system being studied. 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, pouring over Tycho Brahe's tables of planetary positions

and trying to discern the underlying pattern.

Kepler 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

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