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.
Brian Heater for TechCrunch covers how researchers are creating a system that will allow robots to develop motor skills and process abstract concepts. “With this system, the robots can perform complex tasks without getting bogged down in the minutia required to complete them,” Heater writes.
MIT researchers have designed a new chip to enhance the functionality of neural networks while simultaneously reducing the consumption of power, writes Darrell Etherington of TechCrunch. “The basic concept involves simplifying the chip design so that shuttling of data between different processors on the same chip is taken out of the equation,” he explains.
Graduate student Joy Buolamwini tested three different face-recognition systems and found that the accuracy is best when the subject is a lighter skinned man, reports Timothy Revell for New Scientist. With facial recognition software being used by police to identify suspects, “this means inaccuracies could have consequences, such as systematically ingraining biases in police stop and searches,” writes Revell.
Molly Wood at Marketplace speaks with Media Lab graduate student Joy Buolamwini about the findings of her recent research, which examined widespread bias in AI-supported facial recognition programs. “At the end of the day, data reflects our history, and our history has been very biased to date,” Buolamwini said.
Recent research from graduate student Joy Buolamwini shows that facial recognition programs, which are increasingly being used by law enforcement, are failing to identify non-white faces. “When these systems can’t recognize darker faces with as much accuracy as lighter faces, there’s a higher likelihood that innocent people will be targeted by law enforcement,” writes Katharine Schwab for Co. Design.