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Forbes contributor Jennifer Kite-Powell spotlights how MIT researchers created a new AI system that analyzes radio waves bouncing off a person while they sleep to monitor breathing patterns and help identify Parkinson’s disease. “The device can also measure how bad the disease has become and could be used to track Parkinson's progression over time,” writes Kite-Powell.
A new tool for diagnosing Parkinson’s disease developed by MIT researchers uses an AI system to monitor a person’s breathing patterns during sleep, reports Hiawatha Bray for The Boston Globe. “The system is capable of detecting the chest movements of a sleeping person, even if they’re under a blanket or lying on their side,” writes Bray. “It uses software to filter out all other extraneous information, until only the breathing data remains. Using it for just one night provides enough data for a diagnosis.”
Boston Globe reporter Hiawatha Bray speaks with Radio Boston host Tiziana Dearing about how MIT researchers developed an artificial intelligence model that uses a person’s breathing patterns to detect Parkinson’s Disease. The researchers “hope to continue doing this for other diseases like Alzheimer’s and potentially other neurological diseases,” says Bray.
Researchers at MIT have developed an artificial intelligence sensor that can track the progression of Parkinson’s disease in patients based on their breathing while they sleep, reports Conor Hale for Fierce Biotech. “The device emits radio waves and captures their reflection to read small changes in its immediate environment,” writes Hale. “It works like a radar, but in this case, the device senses the rise and fall of a person’s chest.”
MIT researchers have developed a new artificial intelligence system that uses a person’s breathing pattern to help detect Parkinson’s sisease, reports Susannah Sudborough for Boston.com. “The device emits radio signals, analyzes reflections off the surrounding environment, and monitors the person’s breathing patterns without any bodily contact,” writes Sudborough.
Researchers at MIT and other institutions have developed an artificial intelligence tool that can analyze changes in nighttime breathing to detect and track the progression of Parkinson’s disease, reports Casey Ross for STAT. “The AI was able to accurately flag Parkinson’s using one night of breathing data collected from a belt worn around the abdomen or from a passive monitoring system that tracks breathing using a low-power radio signal,” writes Ross.
Prof. Andrew Lo speaks with Forbes contributor Russell Flannery about his work using finance to help lower the cost of drug development for cancer treatment and therapies. “I started thinking about how we could use finance pro-actively to lower the cost of drug development, increase success rates, and make it more attractive for investors,” says Lo. “Because that's really what the issue is: you need investors to come into the space to spend their billions of dollars in order to get these drugs developed.”
Fast Company reporter Elissaveta Brandon writes that a team of scientists from MIT and elsewhere have developed an amphibious artificial vision system inspired by the fiddler crab’s compound eye, which has an almost 360-degree field of view and can see on both land and water. “When translated into a machine,” writes Brandon, “this could mean more versatile cameras for self-driving cars and drones, both of which can become untrustworthy in the rain.”
Wired reporter Will Knight spotlights a study by researchers from MIT and other universities that finds judges are turning to Wikipedia for guidance when making legal decisions. “The researchers also found evidence that the use of Wikipedia reflects an already stretched system,” writes Knight. “The legal decisions that included Wikipedia-influenced citations were most often seen in the lower courts, which they suspect reflects how overworked the judges are.”
The MIT mini cheetah broke a speed record after learning to adapt to difficult terrain and upping its speed, reports Rienk De Beer for Popular Mechanics.
Researchers from CSAIL and elsewhere have found that Irish judges are using Wikipedia articles as a source in their rulings, reports Shane Phelan for Independent. “This work shows that Wikipedia reaches even farther than that, into high-stakes, formalized processes like legal judgments,” says research scientist Neil Thompson. “The worst outcome would be for a judge’s reliance on Wikipedia to lead them to decide a case differently than they would have if they had read either an expert secondary source or the cited precedent itself.”
Researchers from CSAIL, Cornell University, and Maynooth University have released a study concluding that judges in Ireland are utilizing Wikipedia articles to help inform their decisions, reports Colleen Hagerty for Popular Science. Based on their findings, the researchers suggest “the legal community increases its efforts to monitor and fact-check legal information posted on Wikipedia.”
MIT researchers have developed a new system that enabled the mini robotic cheetah to learn to run, reports John Koetsier for Forbes. ““Traditionally, the process that people have been using [to train robots] requires you to study the actual system and manually design models,” explains Prof. Pulkit Agrawal. “This process is good, it’s well established, but it’s not very scalable. “But we are removing the human from designing the specific behaviors.”
A study co-authored by MIT researchers finds that algorithms based on clinical medical notes can predict the self-identified race of a patient, reports Katie Palmer for STAT. “We’re not ready for AI — no sector really is ready for AI — until they’ve figured out that the computers are learning things that they’re not supposed to learn,” says Principal Research Scientist Leo Anthony Celi.
CSAIL graduate student Yunzhu Li and his colleagues have trained a robot to use two metal grippers to mold letters out of play dough, reports Jeremy Hsu for New Scientist. "Li and his colleagues trained a robot to use two metal grippers to mould the approximate shapes of the letters B, R, T, X and A out of Play-Doh," explains Hsu. "The training involved just 10 minutes of randomly manipulating a block of the modelling clay beforehand, without requiring any human demonstrations."