A “scientific sandbox” lets researchers explore the evolution of vision systems
The AI-powered tool could inform the design of better sensors and cameras for robots or autonomous vehicles.
computer vision
Deep-learning model predicts how fruit flies form, cell by cell
The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.
Read MoreTeaching robots to map large environments
A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.
Read More3 Questions: How AI is helping us monitor and support vulnerable ecosystems
MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.
Read MoreMIT tool visualizes and edits “physically impossible” objects
By visualizing Escher-like optical illusions in 2.5 dimensions, the “Meschers” tool could help scientists understand physics-defying shapes and spark new designs.
Read MoreNeural Jacobian Fields, developed by MIT CSAIL researchers, can learn to control any robot from a single camera, without any other sensors.
Read MorePedestrians now walk faster and linger less, researchers find
A computer vision study compares changes in pedestrian behavior since 1980, providing information for urban designers about creating public spaces.
Read MoreMIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.
Read MoreDeveloped to analyze new semiconductors, the system could streamline the development of more powerful solar panels.
Read MoreHave a damaged painting? Restore it in just hours with an AI-generated “mask”
A new method can physically restore original paintings using digitally constructed films, which can be removed if desired.
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