Prof. Regina Barzilay has been named a MacArthur grant recipient for her work in computational linguistics and with applying machine learning to the field of oncology, reports Ellen Gamerman for The Wall Street Journal. “I firmly believe there is a lot of really important information and patterns that are hidden in the data of cancer patients,” said Barzilay.
Boston Globe reporter Laura Crimaldi writes that Prof. Regina Barzilay has been awarded a Macarthur “genius grant” in recognition of her work in the field of computational linguistics. Barzilay said she plans to use the prize, “to continue to work on improving cancer care using machine learning and natural language processing.”
CSAIL researchers have developed a system that detects objects and people hidden around blind corners, writes Anthony Cuthbertson for Newsweek. “We show that walls and other obstructions with edges can be exploited as naturally occurring ‘cameras’ that reveal the hidden scenes beyond them,” says lead author and MIT graduate Katherine Bouman.
Wired reporter Matt Simon writes that MIT researchers have developed a new system that analyzes the light at the edges of walls to see around corners. Simon notes that the technology could be used to improve self-driving cars, autonomous wheelchairs, health care robots and more.
Prof. Joi Ito, director of the Media Lab, is featured on NPR’s TED Radio Hour explaining how he worked with citizen scientists after the 2011 earthquake in Japan to assess damage. To collect data, Ito and his colleagues created Geiger counters, which were “used by ordinary citizens who would just walk around their neighborhoods and measure the radiation,” explains host Guy Raz.
Boston Magazine reporter Jamie Ducharme writes about BioBot Analytics, an MIT startup focused on bringing cities public health information by drawing on the data found in sewage systems. Ducharme writes that by “analyzing samples from the sewer…Biobot is adapting individualized methods of studying the human microbiome” on an urban scale.
Writing for Wired, Prof. Ethan Zuckerman and Chelsea Barabas and Neha Narula of the Digital Currency Initiative address the difficulties in creating decentralized social media networks. “If users have more control of their data, including the right to export and reuse content they’ve created and friends they follow, they’ll be more willing to experiment with new platforms,” the researchers suggest.
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.
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.
In an article for The Washington Post, Stephen Pettigrew and Mayya Komisarchik of the MIT Election Data and Science Lab examine the problem with trying to identify duplicate voter registrations using limited information. They write that, “working with registration records that lack essential details…could cause us to draw wildly inaccurate conclusions about the potential for voter fraud.”
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.
Forbes reporter Kevin Murnane writes about how MIT researchers have used a computer vision system to examine how several American cities physically improved or deteriorated over time. Murnane writes that the study “provides important support for nuanced versions of traditional theories about why urban neighborhoods change over time.”