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Prof. Mohammad Alizadeh and his colleagues "have developed a way to approach network monitoring that provides flexibility in data collection while still keeping both the circuit complexity of the router and the number of external servers low," writes Amy Wallace for UPI.
Two new papers from CSAIL researchers “aim to help doctors make better use of the digital information they’re presented with,” writes Adi Gaskell for The Huffington Post. One examines a tool that uses ICU data to choose the best treatment option based on a range of symptoms, while the other facilitates “predictive models based upon an electronic health record system.”
CSAIL researchers have developed an artificial intelligence system that can reduce video buffering, writes Kevin Murnane for Forbes. The system, “adapts on the fly to current network and buffers conditions,” enabling smoother streaming than other methods.
During this NECN segment, Boston Business Journal reporter Kelly O’Brien discusses the new wireless sleep detection device developed by CSAIL researchers. The algorithm developed by the research team can translate radio waves emitted by the device “into usable information about where a person is in their sleep cycle,” explains O’Brien.
TechCrunch reporter Brian Heater spotlights a new device developed by MIT researchers that can wirelessly monitor sleep. “Thanks to new AI technology, the system is now able to translate subtle movement into meaningful information about the subject’s sleep patterns, including sleep stages (light/deep/R.E.M.), movement and breathing rate."
MIT researchers have developed a new device that can track sleep patterns using radio waves, reports Alyssa Meyers for The Boston Globe. The researchers plan to “use the device to study how Parkinson’s disease affects sleep,” Meyers explains, adding that it could also be helpful with, “studying Alzheimer’s disease, insomnia, sleep apnea, and epilepsy.”
Wired reporter Liz Stinson writes that researchers from MIT and Google have developed a new algorithm that can automatically retouch images on a mobile phone. “The neural network identifies exactly how to make it look better—increase contrast a smidge, tone down brightness, whatever—and apply the changes in under 20 milliseconds,” Stinson explains.
CSAIL researchers have developed an artificial neural network that generates recipes from pictures of food, reports Laurel Dalrymple for NPR. The researchers input recipes into an AI system, which learned patterns “connections between the ingredients in the recipes and the photos of food,” explains Dalrymple.
A team of researchers from MIT and Princeton participating in the Amazon Robotics Challenge are using GelSight technology to give robots a sense of touch, reports Tom Simonite for Wired. Simonite explains that the, “rubbery membranes on the robot’s fingers are tracked from the inside by tiny cameras as they are deformed by objects it touches.”
In this video for USA Today, Sean Dowling highlights Pic2Recipe, the artificial intelligence system developed by CSAIL researchers that can predict recipes based off images of food. The researchers hope the app could one day be used to help, “people track daily nutrition by seeing what’s in their food.”
Researchers at MIT have developed an algorithm that can identify recipes based on a photo, writes BBC News reporter Zoe Kleinman. The algorithm, which was trained using a database of over one million photos, could be developed to show “how a food is prepared and could also be adapted to provide nutritional information,” writes Kleinman.
MIT researchers have developed a new machine learning algorithm that can look at photos of food and suggest a recipe to create the pictured dish, reports Matt Reynolds for New Scientist. Reynolds explains that, “eventually people could use an improved version of the algorithm to help them track their diet throughout the day.”
CSAIL researchers have trained an AI system to look at images of food, predict the ingredients used, and even suggest recipes, writes Matt Burgess for Wired. The system could also analyze meals to determine their nutritional value or “manipulate an existing recipe to be healthier or to conform to certain dietary restrictions," explains graduate student Nick Hynes.
CBS This Morning’s Dana Jacobson explores how MIT researchers are developing technology to enable robots to assist with disaster response, including a robotic cheetah and a system that 3-D prints robots. Prof. Russ Tedrake says that, “there's a natural transition from the robots in the labs now into the robots doing meaningful work.”
CSAIL researchers have developed drones that can drive and fly through a city-like setting, reports Gareth Mitchell for BBC News. The goal for this research is to have the vehicles “coordinate with each other and make intelligent decisions when they fly and drive,” says graduate student Brandon Araki.