When computer vision works more like a brain, it sees more like people do
Training artificial neural networks with data from real brains can make computer vision more robust.
Training artificial neural networks with data from real brains can make computer vision more robust.
Enjoy these recent titles from Institute faculty and staff.
MAGE merges the two key tasks of image generation and recognition, typically trained separately, into a single system.
The first RNA-guided DNA-cutting enzyme found in eukaryotes, Fanzor could one day be harnessed to edit DNA more precisely than CRISPR/Cas systems.
Work could lead to heady applications in novel electronics and more.
Ranking at the top for the 12th year in a row, the Institute also places first in 11 subject areas.
Sihan Chen, a PhD student in MIT's Department of Brain and Cognitive Sciences, studies the social and environmental factors that shape the development of languages.
Scientists find a protein common to flies and people is essential for supporting the structure of axons that neurons project to make circuit connections.
The results could help turn up unconventional superconducting materials.
MIT engineers’ new technology can probe the neural circuits that influence hunger, mood, and a variety of diseases.
New research explores how Dyson maps are putting quantum computers to work in designing fusion energy devices.
By adding weak linkers to a polymer network, chemists dramatically enhanced the material’s resistance to tearing.
The device emits a stream of single photons and could provide a basis for optical quantum computers.
Award recognizes scholars who have the “extraordinary creativity necessary to tackle problems others can’t solve.”