Why did my classifier just mistake a turtle for a rifle?
Two longtime friends explore how computer vision systems go awry.
Two longtime friends explore how computer vision systems go awry.
A course that combines machine learning and health care explores the promise of applying artificial intelligence to medicine.
Professor Adam Chlipala builds tools to help programmers more quickly generate optimized, secure code.
When designing actuators involves too many variables for humans to test by hand, this system can step in.
Along with studying theory, "it's also important to me that the work we are doing will help to solve real-world problems,” says LIDS student Omer Tanovic.
An MIT/IBM system could help artists and designers make quick tweaks to visuals while also helping researchers identify “fake” images.
System lets nonspecialists use machine-learning models to make predictions for medical research, sales, and more.
Developed at MIT Lincoln Laboratory, IdPrism and its award-winning algorithms provide rapid analysis for complex forensic DNA samples.
General-purpose language works for computer vision, robotics, statistics, and more.
MIT Machine Intelligence Community introduces students to nuts and bolts of machine learning.
New approach quickly finds hidden objects in dense point clouds, for use in driverless cars or work spaces with robotic assistants.
System helps machine-learning models glean training information for diagnosing and treating brain conditions.
MIT startup Lumii helps manufacturers replicate the visual effects of holograms on their printed materials.
System automatically writes optimized algorithms to encrypt data in Google Chrome browsers and web applications.
MIT CSAIL system can learn to see by touching and feel by seeing, suggesting future where robots can more easily grasp and recognize objects.