Motherboard reporter Rob Dozier writes about Glitch, an MIT startup that uses machine learning to design clothing. “These tools are meant to empower human designers,” explains graduate student Emily Salvador. “What I think is really cool about these creative-focused AI tools is that there’s still this really compelling need for a human to intervene with the algorithm.”
A new sensory glove developed by MIT researchers provides insight into how humans grasp and manipulate objects, reports The Economist. The glove will not only “be useful in programming robots to mimic people more closely when they pick objects up,” but also could “provide insights into how the different parts of the hand work together when grasping things.”
A new glove embedded with sensors can enable AI systems to identify the shape and weight of different objects, writes HealthDay reporter Dennis Thompson. Using the glove, “researchers have been able to clearly unravel or quantify how the different regions of the hand come together to perform a grasping task,” explains MIT alumnus Subramanian Sundaram.
New Scientist reporter Chelsea Whyte writes that MIT researchers have developed a smart glove that enables neural networks to identify objects by touch alone. “There’s been a lot of hope that we’ll be able to understand the human grasp someday and this will unlock our potential to create this dexterity in robots,” explains MIT alumnus Subramanian Sundaram.
MIT researchers have developed a low-cost electronic glove equipped with sensors that can use tactical information to identify objects, reports Katherine Wu for NOVA Next. Wu writes that the glove is “easy and economical to manufacture, carrying a wallet-friendly price tag of only $10 per glove, and could someday inform the design of prosthetics, surgical tools, and more.”
Researchers from MIT and a number of other institutions have found that grammar-enriched deep learning models had a better understanding of key linguistic rules, reports Kyle Wiggers for VentureBeat. The researchers found that an AI system provided with knowledge of basic grammar, “consistently performed better than systems trained on little-to-no grammar using a fraction of the data, and that it could comprehend ‘fairly sophisticated’ rules.”
In this video, Mashable highlights how CSAIL researchers have developed a new system that can help lift heavy objects by mirroring human activity. The system uses sensors that monitor muscle activity and detect changes in the user’s arm.
In an article for Gizmodo, Dell Cameron writes that graduate student Joy Buolamwini testified before Congress about the inherent biases of facial recognition systems. Buolamwini’s research on face recognition tools “identified a 35-percent error rate for photos of darker skinned women, as opposed to database searches using photos of white men, which proved accurate 99 percent of the time.”
Wired reporter Lily Hay Newman highlights graduate student Joy Buolamwini’s Congressional testimony about the bias of facial recognition systems. “New research is showing bias in the use of facial analysis technology for health care purposes, and facial recognition is being sold to schools,” said Buolamwini. “Our faces may well be the final frontier of privacy.”
Popular Science reporter Rob Verger writes that MIT researchers have developed a new mechanical system that can help humans lift heavy objects. “Overall the system aims to make it easier for people and robots to work together as a team on physical tasks,” explains graduate student Joseph DelPreto.
MIT and the U.S. Air Force “are teaming up to launch a new accelerator focused on artificial intelligence applications,” writes Danny Crichton for TechCrunch. The goal is that projects developed in the MIT-Air Force AI Accelerator would be “addressing challenges that are important to both the Air Force and society more broadly.”
Will Knight writes for MIT Technology Review about the MIT-Air Force AI Accelerator, which “will focus on uses of AI for the public good, meaning applications relevant to the humanitarian work done by the Air Force.” “These are extraordinarily important problems,” says Prof. Daniela Rus. “All of these applications have a great deal of uncertainty and complexity.”
The new MIT-Air Force AI Accelerator “will look at improving Air Force operations and addressing larger societal needs, such as responses to disasters and medical readiness,” reports Breanne Kovatch for The Boston Globe. “The AI Accelerator provides us with an opportunity to develop technologies that will be vectors for positive change in the world,” says Prof. Daniela Rus.
MIT researchers have identified a method to help AI systems avoid adversarial attacks, reports Matthew Hutson for Science. When the researchers “trained an algorithm on images without the subtle features, their image recognition software was fooled by adversarial attacks only 50% of the time,” Hutson explains. “That compares with a 95% rate of vulnerability when the AI was trained on images with both obvious and subtle patterns.”
Inside Higher Ed reporter Lindsay McKenzie writes that a new AI system developed by MIT researchers to summarize the findings of technical scientific papers could “be used in the near future to tackle a long-standing problem for scientists -- how to keep up with the latest research.”