Writing for Gizmodo, Sidney Fussell explains that a new Media Lab study finds facial-recognition software is most accurate when identifying men with lighter skin and least accurate for women with darker skin. The software analyzed by graduate student Joy Buolamwini “misidentified the gender of dark-skinned females 35 percent of the time,” explains Fussell.
CSAIL researchers have developed a new navigation method that allows drones to process less data, have faster reaction times, and dodge obstacles without creating a map of the environment they’re in, writes James Vincent of The Verge. “Because we’re not taking hundreds of measurements and fusing them together, it’s really fast,” said graduate student Peter Florence.
A study co-authored by MIT graduate student Joy Buolamwini finds that facial-recognition software is less accurate when identifying darker skin tones, especially those of women, writes Josh Horwitz of Quartz. According to the study, these errors could cause AI services to “treat individuals differently based on factors such as skin color or gender,” explains Horwitz.
A drone navigation system developed by CSAIL researchers doesn’t rely on intricate maps that show the location of obstacles, but adjusts for uncertainties, reports Martin Finucane of The Boston Globe. The system could be used in “in fields from search-and-rescue and defense to package delivery,” notes Finucane.
Speaking with Rob Verger for Popular Science, Assistant Prof. Christian Catalini explains how it’s not always clear what impacts the value of cryptocurrencies, citing “enthusiasm, hype, maybe even market manipulation.”
Speaking to Bloomberg’s Emily Chang and Selina Wang, Lecturer Luis Perez-Breva suggests that fear of AI stems from confusing it with automation. Perez-Breva explains that in his view, “we need to make better businesses that actually use this technology and AI to take advantage of the automation and create new jobs.”
Graduate student Joy Buolamwini is featured on NPR’s TED Radio Hour explaining the racial bias of facial recognition software and how these problems can be rectified. “The minimum thing we can do is actually check for the performance of these systems across groups that we already know have historically been disenfranchised,” says Buolanwini.
Wired reporter Sandy Ong highlights the work of Prof. Suranga Nanayakkara, who as a postdoc at MIT helped develop the Finger Reader, a device aimed at helping people with visual impairments read without the need for clunky hardware. The Finger Reader, “lets people read only what they’re pointing at, promising a relatively fuss-free experience, especially when out and about.”
Writing for Forbes, Charlie Fink writes about the AR in Action (ARiA) conference, which was held at the MIT Media Lab, and how the event focuses on new ideas rather than products. Fink notes that the conference, “is filled with the innovators and thinkers who are poised to create the next wave of groundbreaking products destined to disrupt the status quo.”
With virtual personal assistants becoming more commonplace, Research Affiliate Jimena Canales suggests in an NPR article that it may be time to reconsider our views of them. Despite knowing that AI is not real, “the boundary between the simulated and the real is as contested as it ever was,” she writes.
Neural networks developed by CSAIL researchers that can identify the contents of images, videos, and audio are the basis for a new system that has added background sound to Google Street View, writes Mark Wilson of Co.Design.
Tony Dokoupil of CBS This Morning visits MIT to learn more about how researchers are working on developing robots that will improve our daily lives. Dokoupil highlights how researchers are, “perfecting the material for a new breed of robot – one that's light and flexible,” adding that the researchers hope, “we'll be able to wear the robot like Tony Stark in ‘Iron Man’."
Hamza Shaban of The Washington Post speaks with Prof. Christian Catalini about how bitcoin fared in 2017. Catalini explains that, “the space is maturing, and that also means more pressure on bitcoin core developers and open-source developers to really get this technology and scale it.”
In an article for The Wall Street Journal, Greg Ip highlights a new study by Prof. Erik Brynjolfsson and graduate student Daniel Rock that examines why advances in technology have not yet led to increases in productivity. Ip writes that, “the authors blame these lags on the cost and time it takes for businesses to adapt to new technologies.”
Fortune reporter David Morris writes that MIT researchers have tricked an artificial intelligence system into thinking that a photo of a machine gun was a helicopter. Morris explains that, “the research points towards potential vulnerabilities in the systems behind technology like self-driving cars, automated security screening systems, or facial-recognition tools.”