Q&A

By training machine learning models with enough examples of basic science, Miles Cranmer hopes to push the pace of scientific discovery forward.

By treating DNA as a language, Brian Hie’s “ChatGPT for genomes” could pick up patterns that humans can’t see, accelerating biological design.

Inside the proton, quarks and gluons shift and morph their properties in ways that physicists are still struggling to understand. Rithya Kunnawalkam Elayavalli brings to the problem a perspective unlike many of their peers.

While some fret about technology’s social impacts, Raj Reddy still believes in the power of artificial intelligence to improve lives.

The mathematician David Bessis claims that everyone is capable of, and can benefit greatly from, mathematical thinking.

Yizhi “Patrick” Cai is coordinating a global effort to write a complete synthetic yeast genome. If he succeeds, the resulting cell will be the artificial life most closely related to humans to date.

To better understand machine learning algorithms, Lenka Zdeborová treats them like physical materials.

Curious connections between physics and math suggest to Latham Boyle that space-time may survive the jump to the next theory of reality.

The philosopher Karen Crowther digs into how the space-time fabric could possibly emerge from something non-spatiotemporal.