A new piece of software has been trained to use wifi signals — which pass through walls, but bounce off living tissue — to monitor the movements, breathing, and heartbeats of humans on the other side of those walls. The researchers say this new tech’s promise lies in areas like remote healthcare, particularly elder care, but it’s hard to ignore slightly more dystopian applications.
Project’s leader Dina Katabi, a 2013 MacArthur “Genius Grant” Fellow who teaches electrical engineering and computer science at MIT, to talk about how the new tech may be used.
She says “We actually are tracking 14 different joints on the body … the head, the neck, the shoulders, the elbows, the wrists, the hips, the knees, and the feet,
“So you can get the full stick-figure that is dynamically moving with the individuals that are obstructed from you — and that’s something new that was not possible before.”
The Problem: identifying human activity from wifi signals isn’t really something that even humans know how to do themselves. So the team developed one A.I. program that monitored human movements with a camera, on one side of a wall, and fed that information to their wifi X-ray A.I., called RF-Pose, as it struggled to make sense of the radio waves passing through that wall on the other side.
The Goal: Katabi would like to get the RF-Pose A.I. sophisticated enough that it can help monitor a variety of human health data tied to movement, identifying the early manifestations and progression of diseases like Parkinson’s or multiple sclerosis (MS). (Prior versions of this research could already track physiological data like breathing patterns and heart rate.) She also said RF-Pose’s underlying tech could easily apply to a number of other potential uses: from search-and-rescue missions retrieving avalanche victims, to wild futuristic revivals of Xbox Kinect, to intervening in dicey hostage situations between terrorists and law enforcement.
Source: MIT Design Lab, powered by Biorealize
These shoes developed by Puma and MIT Design Lab, use bacteria to improve athletic performance.
Puma and MIT Design Lab is developing products with a biological makeup. The idea behind this collaboration is that there is a more complete athletic experience when humans wear living, adaptable products.
“Deep Learning Insoles” and “Breathing Shoes.”
Bacteria is the secret ingredient to the Deep Learning Insoles. Placed inside discreet crevices on the top layer of the insole, bacteria is able to detect compounds present in sweat. The bacteria then responds by changing the conductivity of the insole. The next layer registers these changes. The third and final layer broadcasts the information to the user’s smart device. Users can read all about their fatigue and performance level in real time.
The Breathing Shoe has a biologically active shoe material that is home to microorganisms. The material learns a user’s specific heat patterns and opens up ventilation based on those user-specific heat patterns. Every user winds up with a unique shoe.
Computer scientists have been working on teaching machines to do a wider range of tasks around the house. In a new paper spearheaded by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the University of Toronto, researchers demonstrate “VirtualHome,” a system that can simulate detailed household tasks and then have artificial “agents” execute them, opening up the possibility of one day teaching robots to do such tasks.Using Virtual Home, the MIT computer scientists have trained AI to do about 1,000 different tasks that one might expect a household robot assistant to be able to do. Robots trained in this way may oneday be a great help for the elderly or people who have trouble getting around, and perhaps even to the average person. But until they become useful, at least they’re entertaining to watch.
MIT engineers have devised a 3-D printing technique that uses a new kind of ink made from genetically programmed living cells. Photo: Courtesy of MIT/the researchers
Researchers at the Massachusetts Institute of Technology have developed “living ink” tattoos, which contain genetically programmable living cells. When cells are exposed to different chemicals or molecular compounds, they react, causing parts of the tattoo to light up.
The tattoo is made up of bacteria cells, which the researchers were able to 3D print into the shape of a tree.
Each branch of that tree is sensitive to a different reactor, and when the tattoo is placed on skin that has also been exposed to that same reactor (like a certain chemical), the corresponding branch lights up. They can become wearable sensors.
The Massachusetts Institute of Technology announced on Thursday a new center for autism research, launching with $20 million in initial funding courtesy of Broadcom (brcm) chief executive officer and MIT alum Hock Tan and former investment banker Lisa Yang.
The Hock E. Tan and K. Lisa Yang Center for Autism Research, which will fall under the rubric of MIT’s McGovern Institute for Brain Research, will investigate “the genetic, biological, and neural bases of autism spectrum disorder,” according to MIT. An estimated one of 68 children (and one in 42 boys) in the U.S. are affected by autism, according to the Centers for Disease Control.The Institute draws researchers not only from MIT ranks but from Harvard, biotech companies, and other local institutions, she said. “There’s a collaborative spirit and a lot of cross-pollination with the medical schools. It is not territorial.”
The benefactors, who are parents of two children on the autism spectrum, hope their donation will ignite more support and research for more understanding of the disorder and alleviate its impact on those affected, according to MIT’s statement.
A new MIT study suggests that using carpooling options from companies like Uber and Lyft could reduce the number of vehicles on the road 75 percent without significantly impacting travel time.
The research team led by Professor Daniela Rus of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), developed an algorithm that found that 3,000 four-passenger cars could serve 98 percent of taxi demand in New York City, with an average wait-time of only 2.7 minutes.
“Instead of transporting people one at a time, drivers could transport two to four people at once, results in fewer trips, in less time, to make the same amount of money,” says Rus, who wrote a related paper with former CSAIL postdoc Javier Alonso-Mora, assistant professor Samitha Samaranayake of Cornell University, PhD student Alex Wallar and MIT professor Emilio Frazzoli. “A system like this could allow drivers to work shorter shifts, while also creating less traffic, cleaner air and shorter, less stressful commutes.”
The MIT algorithm is more complex and improves over time, the study’s authors said. And despite the study’s conclusions, they say, it’s not meant to harm the taxi industry. In a phone interview, professor Daniela Rus of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) said the findings simply show a city’s transportation infrastructure could support fewer cars on the road at any given time.
According to their system, instead of working 12-hour shifts, you could work six- or eight-hour shifts. And you would make the same amount of money because it’s the same transportation need, it’s the same level of payment that flows through the system.
Joi Ito, Director of the MIT Media Lab told President Obama that” it may upset some of his students at MIT, but one of his concerns is that it’s been a predominately male gang of kids, mostly white, who are building the core computer science around AI, and they’re more comfortable talking to computers than to human beings. A lot of them feel that if they could just make that science-fiction, generalized AI, we wouldn’t have to worry about all the messy stuff like politics and society. They think machines will just figure it all out for us. They think AI is an answer to “all the messy stuff like politics and society. They think machines will just figure it all out for us… Everybody needs to understand how AI behaves is important… because the question is, how do we build societal values into AI”.
If a health care AI system is designed by white males will it skew towards protecting the health of that group?
If the government uses AI systems for future services will it be fair to everyone?
AI systems learn from studying human behavior;However, there are large differences between people. For example: AI systems serving a Hispanic community will require AI systems trained on that population. It will require ethnic profiling.
Similarly, the best AI systems will understand women’s needs and will learn from profiling that population.
Questionable Issues: Will AI systems for African-Americans have to be developed by African-Americans in order to be accepted? Women for women, etc?