Explainers

One of the quantum fields that fills the universe is special because its default value seems poised to eventually change, changing everything.

These metaphorical gardens have become central objects in modern mathematics.

Neural networks and other forms of machine learning ultimately learn by trial and error, one improvement at a time.

Useful mathematical concepts, like the number line, can linger for millennia before they are rigorously defined.

Making ice requires more than subzero temperatures. The unpredictable process takes microscopic scaffolding, random jiggling and often a little bit of bacteria.

Particle physicists in search of the next theory of reality are consulting a mathematical structure that they know will never fail: a table of possibilities known as the S-matrix.

By making use of randomness, a team has created a simple algorithm for estimating large numbers of distinct objects in a stream of data.

By apparently overtraining them, researchers have seen neural networks discover novel solutions to problems.

When your mind is wandering, your brain’s “default mode” network is active. Its discovery 20 years ago inspired a raft of research into networks of brain regions and how they interact with each other.