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.”
Bloomberg News reporter Carol Massar spotlights how MIT researchers have developed a robot that can identify and sort recyclables. “The system includes a soft Teflon hand that uses tactile sensors to detect the size of an object and the pressure needed to grasp it,” Massar reports. “From there it can determine if it’s made of metal, paper or plastic.”
Research assistant Blakeley Payne speaks with Wall Street Journal reporter Michelle Ma about her work developing a curriculum that teaches kids about the ethics of AI. “You have to integrate the ethics piece at every point, because you never want to fall into the trap of presenting an AI system as like a mathematical equation,” explains Payne, “with the authority of a mathematical equation.”
Fast Company reporter Michael Grothaus writes that CSAIL researchers have developed a deep learning model that could predict whether a woman might develop breast cancer. The system “could accurately predict about 31% of all cancer patients in a high-risk category,” Grothaus explains, which is “significantly better than traditional ways of predicting breast cancer risks.”
A study by MIT researchers examining adversarial images finds that AI systems pick up on tiny details in images that are imperceptible to the human eye, which can lead to misidentification of objects, reports Louise Matsakis for Wired. “It’s not something that the model is doing weird, it’s just that you don’t see these things that are really predictive,” says graduate student Shibani Santurkar.
WCVB-TV’s Jennifer Eagan reports that researchers from MIT and MGH have developed a deep learning model that can predict a patient’s risk of developing breast cancer in the future from a mammogram image. Prof. Regina Barzilay explains that the model “can look at lots of pixels and variations of the pixels and capture very subtle patterns.”
Scientific American reporter Jeremy Hsu highlights how CSAIL researchers have developed a robot that can automatically sort recycling. The robot “uses soft Teflon ‘fingers,’ which have fingertip sensors to detect object size and stiffness,” Hsu explains.
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.
HealthDay News reporter Amy Norton writes that MIT researchers have developed an AI system that can help predict a woman’s risk of developing breast cancer and provide more personalized care. “If you know a woman is at high risk, maybe she can be screened more frequently, or be screened using MRI,” explains graduate student Adam Yala.
Technology Review reporter Will Knight spotlights how MIT researchers have developed a new chip that is many times more efficient than silicon chips and could help bring AI to a multitude of devices where power is limited. “We need new hardware because Moore’s law has slowed down,” explains Prof. Vivienne Sze.