Verge reporter James Vincent writes that Media Lab researchers have found that the facial recognition system Rekognition performed worse at identifying an individual’s gender if they were female or dark-skinned. In experiments, the researchers found that the system “mistook women for men 19 percent of the time and mistook darker-skinned women for men 31 percent of the time,” Vincent explains.
MIT researchers have found that the Rekognition facial recognition system has more difficulty identifying the gender of female and darker-skinned faces than similar services, reports Natasha Singer for The New York Times. Graduate student Joy Buolamwini said “the results of her studies raised fundamental questions for society about whether facial technology should not be used in certain situations,” writes Singer.
New York Times reporter Steve Lohr writes about the MIT AI Policy Conference, which examined how society, industry and governments should manage the policy questions surrounding the evolution of AI technologies. “If you want people to trust this stuff, government has to play a role,” says CSAIL principal research scientist Daniel Weitzner.
TechCrunch reporter John Biggs writes that MIT researchers have developed a new system that allows users to reverse-engineer complex items by deconstructing objects and turning them into 3-D models. Biggs writes that the system is a “surprisingly cool way to begin hacking hardware in order to understand it’s shape, volume and stability.”
Writing for Wired, Prof. Carlo Ratti predicts that in 2019 researchers will develop new methods for allowing people to use the internet in less intrusive ways. “The internet of things will continue to grow, and we will work out more ways to develop ‘things’ that allow us to enjoy the internet without being overwhelmed by it,” writes Ratti.
In a piece about the growing field of origami, The Economist highlights Prof. Erik Demaine’s work proving that “any straight-sided figure—an octagon, a cityscape silhouette or a blocky Bart Simpson—can be extracted with exactly one straight cut if you fold the paper up the right way first.”
Provost Martin Schmidt and SHASS Dean Melissa Nobles speak with Wall Street Journal reporter Sara Castellanos about MIT’s efforts to advance the study of AI and its ethical and societal implications through the MIT Stephen A. Schwarzman College of Computing. Schmidt says this work “requires a deep partnership between the technologists and the humanists.”
Boston Globe reporter Jessie Scanlon spotlights Prof. Regina Barzilay’s work developing machine learning systems that can identify patients at risk of developing breast cancer. Barzilay is creating “software that aims to teach a computer to analyze mammogram images more effectively than the human eye can and to catch signs of cancer in its earliest phases.”
MIT researchers have found that it’s easy to reidentify anonymized data compiled in massive datasets, reports Kelsey Campbell-Dollaghan for Fast Company. The findings show that urban planners, tech companies and designers, “who stand to learn so much from these big urban datasets,” writes Campbell-Dollaghan, “need to be careful about whether all that data could be combined to deanonymize it.”
Graduate student Irene Chen speaks with WGBH’s Living Lab Radio about her work trying to reduce bias in health care algorithms. “The results that we’ve shown from healthcare algorithms are so powerful that we really do need to see how we could implement those carefully, safely, robustly and fairly,” she explains.
A new study by MIT researchers provides evidence that compiling massive anonymized datasets of people’s movement patterns can put their private data at risk, reports the Xinhua news agency. The researchers found “data containing ‘location stamps’ – information with geographical coordinates and time stamps – could be used to easily track the mobility trajectories of how people live and work.”
Prof. Aleksander Madry and graduate student Anish Athalye speak with BBC News reporter Linda Geddes about how AI systems can be tricked into seeing or hearing things that aren’t actually there. “People are looking at it as a potential security issue as these systems are increasingly being deployed in the real world,” Athalye explains.
In an article for Forbes, Andrew Raupp highlights a pilot program debuted by MIT last year that allows students the option to receive a tamper-free version of their diploma digitally using Bitcoin’s blockchain technology. Raupp writes that, “Unlike a paper diploma, which could be easily lost or falsified, blockchain ensures that this important piece of data is never lost.”
CSAIL researchers have developed a new technique to recreate paintings from a single photograph, reports John Biggs for TechCrunch. “The project uses machine learning to recreate the exact colors of each painting and then prints it using a high-end 3D printer that can output thousands of colors using half-toning,” Biggs explains.
Forbes reporter Samar Marwan speaks with Rana el Kaliouby, CEO and cofounder of the MIT startup Affectiva, about her work developing new technology that can read human facial expressions. Marwan explains that el Kaliouby and Prof. Rosalind Picard started developing the technology at MIT, “to focus on helping children on the autism spectrum better understand how other people were feeling.”