In Theory

It was a big year. Researchers found a way to idealize deep neural networks using kernel machines—an important step toward opening these black boxes. There were major developments toward an answer about the nature of infinity. And a mathematician finally managed to model quantum gravity.

It was a big year. Fermilab discovered possible evidence of new physics with the muon G-2 experiment. Physicists created a time crystal, a new phase of matter that appears to violate one of nature’s most cherished laws. And we got a glimpse of an enormous pair of bubbles towering over the Milky Way.

A paradigm shift in how we think about the functions of the human brain. A long-awaited genetic sequence of Rafflesia arnoldii, the strangest flower in the world. A revelation in sleep science. These are some of the year’s biggest discoveries in neuroscience and other areas of biology.

The James Webb Space Telescope is like nothing ever launched into space. It could explore the universe’s very first stars, uncover evidence of extraterrestrial life — or literally hit a snag and become worthless.

Anne Carpenter, senior director of the Imaging Platform of the Broad Institute of the Massachusetts Institute of Technology and Harvard University, describes how her interest in developing new medicines led her to work at the interface of biology and computer science.

Laura Kreidberg discusses how she’ll use NASA’s James Webb Space Telescope to peer into the skies of rocky planets orbiting other stars and look for signs of habitability, or even life.

The neuroscientist Anil Seth of the University of Sussex discusses the principles, philosophy and experimentation that have brought scientists closer to understanding the phenomenon of consciousness.

The genomics researcher Karen Miga explains why it’s so important to learn the DNA sequence information buried in parts of our genome that were resistant to scrutiny.

Melanie Mitchell, the Davis professor of complexity at the Santa Fe Institute, has worked on digital minds for decades. She says AI will never truly be “intelligent” until they can do something uniquely human: make analogies.