MIT researchers have developed a model that recovers valuable data lost from images and video that have been “collapsed” into lower dimensions.
The model could be used to recreate video from motion-blurred images, or from new types of cameras that capture a person’s movement around corners but only as vague one-dimensional lines. While more testing is needed, the researchers think this approach could someday could be used to convert 2D medical images into more informative — but more expensive — 3D body scans, which could benefit medical imaging in poorer nations.
“There’s a growing concern about machine-generated fake text, and for a good reason,” says CSAIL PhD student Tal Schuster, lead author on a new paper on their findings. “I had an inkling that something was lacking in the current approaches to identifying fake information by detecting auto-generated text — is auto-generated text always fake? Is human-generated text always real?”
If you’ve ever seen a self-driving car in the wild, you might wonder about that spinning cylinder on top of it.
It’s a “lidar sensor,” and it’s what allows the car to navigate the world. By sending out pulses of infrared light and measuring the time it takes for them to bounce off objects, the sensor creates a “point cloud” that builds a 3D snapshot of the car’s surroundings.
Assessing placental health is difficult because of the limited information that can be gleaned from imaging. Traditional ultrasounds are cheap, portable, and easy to perform, but they can’t always capture enough detail. This has spurred researchers to explore the potential of magnetic resonance imaging (MRI). Even with MRIs, though, the curved surface of the uterus makes images difficult to interpret.
Existing efforts to detect IP hijacks tend to look at specific cases when they’re already in process. But what if we could predict these incidents in advance by tracing things back to the hijackers themselves?
Cities are now beginning to question how much citizen data, if any, they can use to track government operations. In a new study, MIT researchers find that there is, in fact, a way for cities to preserve citizen privacy while using their data to improve efficiency.
A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient’s risk of cardiovascular death.
Josh Tenenbaum, a professor in MIT’s Department of Brain and Cognitive Sciences who studies human cognition, has been named a recipient of a 2019 MacArthur Fellowship.
An MIT student has invented a novel algorithm that produces a portamento effect between any two audio signals in real-time. In experiments, the algorithm seamlessly merged various audio clips, such as a piano note gliding into a human voice, and one song blending into another.
A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has brought us closer to this chameleon reality, by way of a new system that uses reprogrammable ink to let objects change colors when exposed to ultraviolet (UV) and visible light sources.
