Demystifying the world of deep networks
Researchers discover that no magic is required to explain why deep networks generalize despite going against statistical intuition.
Center for Brains Minds and Machines
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.
This object-recognition dataset stumped the world’s best computer vision models
Objects are posed in varied positions and shot at odd angles to spur new AI techniques.
Helping machines perceive some laws of physics
Model registers “surprise” when objects in a scene do something unexpected, which could be used to build smarter AI.
Materials Day talks examine the promises and challenges of AI and machine learning
The ability to predict and make new materials faster highlights the need for safety, reliability, and accurate data.
Josh Tenenbaum receives 2019 MacArthur Fellowship
Brain and cognitive sciences professor studies how the human mind is able to learn so rapidly.
Perception of musical pitch varies across cultures
How people interpret musical notes depends on the types of music they have listened to, researchers find.
Hearing through the clatter
Study reveals brain regions that respond differently to the presence of background noise, suggesting the brain progressively hones in on and isolates sounds.
Toward artificial intelligence that learns to write code
Researchers combine deep learning and symbolic reasoning for a more flexible way of teaching computers to program.
Putting vision models to the test
Study shows that artificial neural networks can be used to drive brain activity.
For better deep neural network vision, just add feedback (loops)
The DiCarlo lab finds that a recurrent architecture helps both artificial intelligence and our brains to better identify objects.
Josh Tenenbaum named Innovator of the Year by R&D Magazine
Professor honored for work on the nature and origins of intelligence in the human mind and applying that knowledge to build human-like intelligence in machines.
Machines that learn language more like kids do
Computer model could improve human-machine interaction, provide insight into how children learn language.
Fujitsu Laboratories and MIT’s Center for Brains, Minds and Machines Broaden Partnership
New investment supports intelligence research, student fellowships.