In an article for Financial Times, CSAIL Director Daniela Rus explains why humans should collaborate rather than compete with AI. “Technology and people do not have to be in competition,” writes Rus. “Collaborating with AI systems, we can augment and amplify many aspects of work and life.”
Lecturer Luis Perez-Breva writes for Quartz about why most retail corporations’ definition of AI is flawed. “'AI' is at its best when we program it to address problems that are hard for humans; when not used to upskill humans, however, all it does is shift work from employees to customers,” Perez-Breva writes.
A robotic carpenter developed by CSAIL is pre-cutting wood for flat-pack furniture, making assembly safer and more efficient. Called AutoSaw, the idea “was not to replace human carpenters but to allow them to focus on more important tasks such as design,” writes Dave Lee for the BBC.
AI leader SenseTime is the first company to join the MIT Intelligence Quest since its launch, writes Xinhua editor Xiang Bo. “As the largest provider of AI algorithms in China, we are very excited to work with MIT to lead global AI research into the next frontier,” said Xu Li, CEO of SenseTime.
David Grossman of Popular Mechanics writes about AutoSaw, a system developed by CSAIL researchers that assists in custom build carpentry projects. The system is designed “to split the difference between machine-built quality and unique customization” and requires human assembly after the pieces are cut, explains Grossman.
Autosaw, the robotic carpenter developed by researchers from CSAIL, can cut pieces for furniture building, as long as you provide the raw materials. “It’ll cut pieces to shape, drill the necessary holes and even move them around the workshop for you,” writes Thomas Tamblyn for Huff Post.
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."
AutoSaw, developed in CSAIL, is “a new system of robot-assisted carpentry that could make the creation of custom furniture and fittings safer, easier, and cheaper,” writes James Vincent of The Verge. As postdoc Jeffrey Lipton explains, AutoSaw “shows how advanced robotics could fit into the workflow of a carpenter or joiner.”
Using a modified Roomba vacuum, CSAIL researchers are able to autonomously cut pieces of wood for assembling furniture, writes Leah Crane for New Scientist. “Two lifting robots pick up a piece of wood, bring it over to a chop saw, and hold it in place while the saw cuts it to size,” Crane explains.
CSAIL postdoc Jeffrey Lipton, along with Prof. Daniela Rus and PhD candidate Adriana Schulz, has developed AutoSaw, a software-driven carpentry system that readies wood pieces for hand assembly, writes Mark Wilson of Co.Design. “We’re moving toward a new manufacturing revolution with 3D printers and robots to make objects with unprecedented complexity,” says Schulz.
Spencer Buell of Boston Magazine speaks with graduate student Joy Buolamwini, whose research shows that many AI programs are unable to recognize non-white faces. “‘We have blind faith in these systems,’ she says. ‘We risk perpetuating inequality in the guise of machine neutrality if we’re not paying attention.’”
Aerobotics, a startup by MIT alumnus James Paterson ’14 aims to optimize crop yields and reduce costs for farmers by using an app to analyze images of the land. “Satellite footage is used to highlight longer-term trends, while drones are flown at specific points during the season to get more detailed information,” write Eleni Giokos and Mary McDougall for CNN Tech.
An article in The Economist states that new research by MIT grad student Joy Buolamwini supports the suspicion that facial recognition software is better at processing white faces than those of other people. The bias probably arises “from the sets of data the firms concerned used to train their software,” the article suggests.
Dave Gershgorn writes for Quartz, highlighting congress’ concerns around the dangers of inaccurate facial recognition programs. He cites Joy Buolamwini’s Media Lab research on facial recognition, which he says “maintains that facial recognition is still significantly worse for people of color.”
A new paper from graduate students in EECS details a newly-developed chip that allows neural networks to function offline, while drastically reducing power usage. “That means smartphones and even appliances and smaller Internet of Things devices could run neural networks locally” writes Eric Mack for Forbes.