Neuroscientists find a way to make object-recognition models perform better
Adding a module that mimics part of the brain can prevent common errors made by computer vision models.
How humans use objects in novel ways to solve problems
What's SSUP? The Sample, Simulate, Update cognitive model developed by MIT researchers learns to use tools like humans do.
Neural pathway crucial to successful rapid object recognition in primates
Recurrent processing via prefrontal cortex, necessary for quick visual object processing in primates, provides a key insight for developing brain-like artificial intelligence.
How we make moral decisions
In some situations, asking “what if everyone did that?” is a common strategy for judging whether an action is right or wrong.
National Science Foundation announces MIT-led Institute for Artificial Intelligence and Fundamental Interactions
IAIFI will advance physics knowledge — from the smallest building blocks of nature to the largest structures in the universe — and galvanize AI research innovation.
Study suggests animals think probabilistically to distinguish contexts
New statistical model may help scientists understand how animals infer whether surroundings are novel or haven’t changed enough to be a new context.
Key brain region was “recycled” as humans developed the ability to read
Part of the visual cortex dedicated to recognizing objects appears predisposed to identifying words and letters, a study finds.
Looking into the black box
Recent advances give theoretical insight into why deep learning networks are successful.
Nine MIT School of Science professors receive tenure for 2020
Professors earn tenure in the departments of Brain and Cognitive Sciences, Chemistry, Mathematics, and Physics.
Universal musical harmony
Acoustic and biological constraints shape how we hear harmony across cultures.
Eleven from MIT awarded 2020 Fulbright Fellowships
Graduating seniors and recent alumni will spend upcoming year abroad on Fulbright grants.
A new model of vision
Computer model of face processing could reveal how the brain produces richly detailed visual representations so quickly.
Demystifying the world of deep networks
Researchers discover that no magic is required to explain why deep networks generalize despite going against statistical intuition.
Bridging the gap between human and machine vision
Researchers develop a more robust machine-vision architecture by studying how human vision responds to changing viewpoints of objects.
Differences between deep neural networks and human perception
Stimuli that sound or look like gibberish to humans are indistinguishable from naturalistic stimuli to deep networks.