  Optical tweezers exploit light--matter interactions to trap particles ranging
from single atoms to micrometer-sized eukaryotic cells. For this reason,
optical tweezers are a ubiquitous tool in physics, biology, and nanotechnology.
Recently, the use of deep learning has started to enhance optical tweezers by
improving their design, calibration, and real-time control as well as the
tracking and analysis of the trapped objects, often outperforming classical
methods thanks to the higher computational speed and versatility of deep
learning. Here, we review how deep learning has already remarkably improved
optical tweezers, while exploring the exciting, new future possibilities
enabled by this dynamic synergy. Furthermore, we offer guidelines on
integrating deep learning with optical trapping and optical manipulation in a
reliable and trustworthy way.
