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A video from Financial Times highlights work being done by CSAIL to develop robot teams. Prof. Daniela Rus discusses how partnering robots has the potential to “form much more adaptive and complex systems that will be able to take on a wider set of tasks."
NBC Mach’s Denise Chow writes about MIT research being done to develop color-changing, 3D printed clothing using UV light. “We wanted to know, how can we update the color of an object at any time?” said Prof. Stephanie Mueller.
Kaya Yurieff reports for CNN that CSAIL researchers have developed a system that allows the colors of 3-D printed objects to be altered after they have been fabricated. Prof. Stefanie Mueller explains that, “this sort of technology could help minimize the amount of waste that is produced from updating products."
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.”
TechCrunch reporter Devin Coldewey writes about a new game developed by Prof. D Fox Harrell that is aimed at “identifying and navigating the subtleties of sexism” in the workplace.
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’."
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.