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 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.”
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.
WCVB-TV’s Mike Wankum visits the Media Lab to learn more about a new wearable device that allows users to communicate with a computer without speaking by measuring tiny electrical impulses sent by the brain to the jaw and face. Graduate student Arnav Kapur explains that the device is aimed at exploring, “how do we marry AI and human intelligence in a way that’s symbiotic.”
Fast Company reporter Eillie Anzilotti highlights how MIT researchers have developed an AI-enabled headset device that can translate silent thoughts into speech. Anzilotti explains that one of the factors that is motivating graduate student Arnav Kapur to develop the device is “to return control and ease of verbal communication to people who struggle with it.”
MIT researchers have developed a new system to 3-D print scaffolding for biological cultures, making it possible to grow uniform cells with specific functions, reports The Economist. “This discovery could help those trying to find ways of encouraging stem cells to generate tissue and organs for transplant.”
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.”
Researchers at MIT have found that adversarial examples, a kind of optical illusion for AI that makes the system incorrectly identify an image, may not actually impact AI in the ways computer scientists have previously thought. “When algorithms fall for an adversarial example, they’re not hallucinating—they’re seeing something that people don’t,” Louise Matsakis writes for Wired.
MIT researchers have identified a new method to engineer neural networks in a way that allows them to be a tenth of the size of current networks without losing any computational ability, reports Avery Thompson for Popular Mechanics. “The breakthrough could allow other researchers to build AI that are smaller, faster, and just as smart as those that exist today,” Thompson explains.
WBUR reporter Pamela Reynolds highlights graduate student Joy Buolamwini’s piece, “The Coded Gaze,” which is currently on display as part of the “Avatars//Futures” exhibit at the Nave Gallery. Reynolds writes that Buolamwini’s piece “questions the inherent bias of coding in artificial intelligence, which has resulted in facial recognition technology unable to recognize black faces.”
Inside Science reporter Yuen Yiu writes that MIT researchers have developed a new AI system that can summarize scientific research papers filled with technical terms. Yiu writes that the system “is a dramatic improvement from current programs, and could help scientists or science writers sift through large numbers of papers for the ones that catch their interest.”