Neural networks

Mathematicians and neuroscientists have created the first anatomically accurate model that explains how vision is possible.

The computer vision scientist Greg Johnson is building systems that can recognize organelles on sight and show the dynamics of living cells more clearly than microscopy can.

To researchers’ surprise, deep learning vision algorithms often fail at classifying images because they mostly take cues from textures, not shapes.

New experimental results simultaneously advance and challenge the theory that the brain’s network of neurons balances on the knife-edge between two phases.

The latest AI algorithms are probing the evolution of galaxies, calculating quantum wave functions, discovering new chemical compounds and more. Is there anything that scientists do that can’t be automated?

Neural networks can be as unpredictable as they are powerful. Now mathematicians are beginning to reveal how a neural network’s form will influence its function.

Neural networks are famously incomprehensible, so Been Kim is developing a “translator for humans.”

A visual prank exposes an Achilles’ heel of computer vision systems: Unlike humans, they can’t do a double take.

Machine learning techniques are commonly based on how the visual system processes information. To beat their limitations, scientists are drawing inspiration from the sense of smell.