No one yet knows which technology will power the quantum computers of the future, but the race to create them has already produced some of science’s most intricate machinery.

Computer scientist Melanie Mitchell discusses why artificial intelligence doesn’t “think” or “reason” like humans, and how we can create better methods for measuring machine cognition.

For the first time in 30 years, computer scientists have found a better way to allocate objects evenly between two groups.

An updated view of categorization reflects the modern understanding that the nervous system is more of a prediction engine than a filing cabinet.

To explore quantum phenomena, we must leave the familiar world and enter the abstract realm of Hilbert space.

The theoretical side of the field doesn’t require computing machines. But many questions would never have been posed without them.

Mathematicians found that a broad class of networks will abruptly shift behavior past a critical point.

By doubling their entire genome, organisms can evolve extremely rapidly — or they can lose it all. New studies reveal how to survive the high-risk, high-reward evolutionary event.

Sarah Demers, chair of the physics department at Yale University, sees the potential for large language models to both help and harm her field.

In this special live recording from the International Congress of Mathematicians in Philadelphia, July 2026, the central question discussed was what do mathematicians really value about doing mathematics in the first place?

Mathematicians at OpenAI showed that the Navier-Stokes equations, which describe how fluids flow, can sometimes “blow up.” But the massive result is not without controversy.

Hidden correspondences hint at a deeper structure to the mathematical universe. Our columnist unpacks one of those connections and asks mathematicians what they might mean.