MIT researchers have created a new system that enables robots to identify objects using tactile information, reports Darrell Etherington for TechCrunch. “This type of AI also could be used to help robots operate more efficiently and effectively in low-light environments without requiring advanced sensors,” Etherington explains.
Fast Company reporter Michael Grothaus writes that CSAIL researchers have developed a new system that allows robots to determine what objects look like by touching them. “The breakthrough could ultimately help robots become better at manipulating objects,” Grothaus explains.
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.”
Wired reporter Aarian Marshall spotlights how Prof. Sarah Williams has been developing digital tools to help map bus routes in areas that lack transportation maps. “The maps show that there is an order,” Williams explains. “There is, in fact, a system, and the system could be used to help plan new transportation initiatives.”
A study by MIT researchers examines the historical impact of technology on the labor market in an attempt to better understand the potential effect of AI systems, reports Adi Gaskell for Forbes. “The authors propose a number of solutions for improving data on the skills required in the workforce today, and from that the potential for AI to automate or augment those skills,” Gaskell explains.
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.”
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.”
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.
In an article about how the social messaging app WhatsApp could have a large influence on the upcoming election in India, the Financial Times spotlights postdoctoral associate Kiran Garimella’s work examining how misinformation spreads in India through platforms such as WhatsApp.
Financial Times reporter Hugo Cox highlights how MIT researchers have developed robots that can be used to detect disease in specific regions by sampling sewage. “A local robot takes days to identify an outbreak of flu; the surge in attendance at local hospitals and surgeries typically takes weeks to register,” Cox explains. “And because the information is local, the response can be too.”
Prof. Regina Barzilay speaks with NPR reporter Richard Harris about her work developing AI systems aimed at improving identification of breast cancer in mammograms, inspired by her experience with the disease. “At every point of my treatment, there would be some point of uncertainty, and I would say, 'Gosh, I wish we had the technology to solve it,’” says Barzilay.
Forbes contributor Charles Towers-Clark writes that CSAIL researchers have developed a new machine learning system that could be used to help develop better estimates about internet data. “In tests, the system was over 57% more accurate in estimating internet traffic and more than 71% for trending social media topics,” Towers-Clark explains.