Researchers at MIT and Harvard Medical School have created an artificial intelligence program that can accurately identify a patient’s race based off medical images, reports Tony Ho Tran for The Daily Beast. “The reason we decided to release this paper is to draw attention to the importance of evaluating, auditing, and regulating medical AI,” explains Principal Research Scientist Leo Anthony Celi.
CSAIL researchers have developed a robotic arm equipped with a sensorized soft brush that can untangle hair, reports Douglas Belkin for The Wall Street Journal. “The laboratory brush is outfitted with sensors that detect tension," writes Belkin. “That tension reads as pain and is used to determine whether to use long strokes or shorter ones.”
TechCrunch reporter Kyle Wiggers spotlights how MIT researchers have developed a new computer vision algorithm that can identify images down to the individual pixel. The new algorithm is a “vast improvement over the conventional method of ‘teaching’ an algorithm to spot and classify objects in pictures and videos,” writes Wiggers.
TechCrunch reporter Brian Heater spotlights new MIT robotics research, including a team of CSAIL researchers “working on a system that utilizes a robotic arm to help people get dressed.” Heater notes that the “issue is one of robotic vision — specifically finding a method to give the system a better view of the human arm it’s working to dress.”
MIT researchers have developed reconfigurable, self-assembling robotic cubes embedded with electromagnets that allow the robots to easily change shape, reports John Koetsier for Forbes. “If each of those cubes can pivot with respect to their neighbors you can actually reconfigure your first 3D structure into any other arbitrary 3D structure,” explains graduate student Martin Nisser.
MIT researchers have utilized a new reinforcement learning technique to successfully train their mini cheetah robot into hitting its fastest speed ever, reports Matt Simon for Wired. “Rather than a human prescribing exactly how the robot should walk, the robot learns from a simulator and experience to essentially achieve the ability to run both forward and backward, and turn – very, very quickly,” says PhD student Gabriel Margolis.
MIT researchers have created a new computer algorithm that has allowed the mini cheetah to maximize its speed across varying types of terrain, reports Shi En Kim for Popular Science. “What we are interested in is, given the robotic hardware, how fast can [a robot] go?” says Prof. Pulkit Agrawal. “We didn’t want to constrain the robot in arbitrary ways.”
MIT researchers have used a new reinforcement learning system to teach robots how to acclimate to complex landscapes at high speeds, reports Emmett Smith for Mashable. “After hours of simulation training, MIT’s mini-cheetah robot broke a record with its fastest run yet,” writes Smith.
CSAIL researchers developed a new machine learning system to teach the MIT mini cheetah to run, reports James Vincent for The Verge. “Using reinforcement learning, they were able to achieve a new top-speed for the robot of 3.9m/s, or roughly 8.7mph,” writes Vincent.
Graduate student Matt Groh speaks with Scientific American reporter Sarah Vitak about his team’s work studying whether human detection or artificial intelligence is better at identifying deepfakes and misinformation online. “One of the things that we would suggest for the future development of these systems is trying to figure out ways to explain why the AI is making a decision,” says Groh.
Researchers from MIT and journalists from STAT conducted a months long investigation and found that “subtle shifts in data fed into popular health care algorithms — used to warn caregivers of impending medical crises — can cause their accuracy to plummet over time, raising the prospect AI could do more harm than good in many hospitals,” reports Casey Ross for STAT.
Popular Science reporter Tatyana Woodall writes that CSAIL researchers have developed electromagnetic bot blocks that can reconfigure into various shapes and could potentially help astronauts build in space. “The electromagnetic lining of the 3D printed frames allows cubes to seamlessly attract, repel, or even turn themselves off,” writes Wood. “One cube takes a little over an hour to make, and only costs 60 cents.”
Physics World reporter Tim Wogan writes that MIT researchers used machine learning techniques to identify a mysterious “X” particle in the quark–gluon plasma produced by the Large Hadron Collider. “Further studies of the particle could help explain how familiar hadrons such as protons and neutrons formed from the quark–gluon plasma believed to have been present in the early universe,” writes Wogan.
Using machine learning techniques, MIT researchers have detected “X particles” produced by the Large Hadron Collider, reports Rahul Rao for Popular Science. “The results tell us more about an artifact from the very earliest ticks of history, writes Rao. “Quark-gluon plasma filled the universe in the first millionths of a second of its life, before what we recognize as matter—molecules, atoms, or even protons or neutrons—had formed.”
Scientists have discovered “X-particles” in the aftermath of collisions produced in the Large Hadron Collider, which could shed light on the structure of these elusive particles, reports Becky Ferreira for Vice. “X particles can yield broader insights about the type of environment that existed in those searing and turbulent moments after the Big Bang,” writes Ferreira.