Electrical engineering and computer science (EECS)

TechCrunch reporter Brian Heater writes that MIT researchers have created a new system that allows users to change the color of 3-D printed objects. Heater explains that researchers, “are looking to bring color-changing properties to the 3D-printing process in an attempt to help reduce material waste.”

Marc Bain of Quartz reports that CSAIL researchers have created a system that changes the color of 3-D printed objects using UV light. The researchers hope this system will allow consumers to, “quickly match accessories to outfits, or let retail stores switch the color of clothing or other items on the spot for customers,” explains Bain. 

CSAIL researchers have developed a method that allows the color of 3-D printed objects to change after they have been printed, writes Emily Matchar for Smithsonian. The method uses, “UV light to change the pixels on an object from transparent to colored, and then a regular office projector to turn them from colored to transparent,” explains Prof. Stefanie Mueller.

Wired reporter Arielle Pardes Gear writes that CSAIL researchers have developed a new system, called ColorFab, that makes it possible to change the color of 3-D printed objects after they have been created. ColorFab allows users to change an object’s color, “by returning to the ColorFab interface, selecting the areas to recolor, and then activating those areas with UV light.”

MIT researchers are stress-testing AI systems by tricking them into misidentifying images, writes Dana Smith of Scientific American. Graduate student Anish Athalye notes that some neural nets are outperforming humans, “but they have this weird property that it seems that we can trick them pretty easily.”

Graduate student Achuta Kadambi speaks with the BBC’s Gareth Mitchell about the new depth sensors he and his colleagues developed that could eventually be used in self-driving cars. “This new approach is able to obtain very high-quality positioning of objects that surround a robot,” Kadambi explains. 

Times Higher Ed reporter Matthew Reisz highlights Prof. Daniel Jackson’s book, “Portraits of Resilience.” Reisz writes that, “MIT and its press are to be congratulated on a book – given out free to all this year’s new students – that not only addresses head on the issue of mental health within higher education but is so frank about how this plays out within its own institution.”

In a Vox article about the increasing scalability of solar photovoltaic power, David Roberts highlights solar cells developed by Prof. Vladimir Bulovic. The solar cells are, “so small and light they could sit atop a soap bubble without popping it,” explains Roberts.

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’."

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.”

In an article for The Wall Street Journal, Visiting Lecturer Irving Wladawsky-Berger spotlights MIT’s AI and the Future of Work Conference. Wladawsky-Berger writes that participants, “generally agreed that AI will have a major impact on jobs and the very nature of work. But, for the most part, they viewed AI as mostly augmenting rather than replacing human capabilities.”

Abigail Beall of New Scientist writes that MIT researchers have developed an algorithm that can trick an AI system, highlighting potential weaknesses in new image-recognition technologies used in everything from self-driving cars to facial recognition systems. “If a driverless car failed to spot a pedestrian or a security camera misidentified a gun the consequences could be incredibly serious.” 

CSAIL researchers have tricked a machine-learning algorithm into misidentifying an object, reports Louise Matsakis for Wired. The research, “demonstrates that attackers could potentially create adversarial examples that can trip up commercial AI systems,” explains Matsakis. 

CSAIL researchers have discovered that some traffic jams are caused by tailgating, writes Thomas Tamblyn for HuffPost. Maintaining an equal distance in front of and behind a vehicle, “could have a dramatic effect in reducing travel time and fuel consumption without having to build more roads or make other changes to infrastructure,” explains Prof. Berthold Horn. 

Forbes reporter Laurie Winkless writes that MIT researchers have found that if drivers maintained fixed distances between the cars in front of and behind them they would be able to reduce traffic jams. “We humans tend to view the world in terms of what’s ahead of us, so it might seem counter-intuitive to look backwards,” explains Prof. Berthold Horn.