Research study reveals that there is severe shortage of qualified talent in the country has left over 4,000 mid and senior-level job positions vacant in the artificial intelligence (AI) sector. Around 57 per cent of firms hiring for AI roles are looking for candidates with over five years of experience, while the average AI candidates has three years of experience.
It seems that academic and training programs just can’t keep up with the pace of innovation and new discoveries with AI. Not only do AI professionals need official training, they need on-the-job experience. Therefore, there aren’t enough experienced AI professionals to step into the leadership roles required by organizations who are just beginning to adopt AI strategies into their operations.
MIT wants to solve the problem and has announced a $1 billion initiative to start a new college of computing to train the next generation of machine learning
An MIT study has revealed the way artificial intelligence system collect data often makes them racist and sexist.
Researchers looked at a host of systems, and found many of them exhibited a outrageous bias.
The team then developed system to help researchers make sure their systems are less biased.
Computer scientists are often quick to say
that the way to make these systems less biased is to simply design
better algorithms,’ said lead author Irene Chen, a PhD student who wrote
the paper with MIT professor David Sontag and postdoctoral associate
Fredrik D. Johansson.
algorithms are only as good as the data they’re using, and our research
shows that you can often make a bigger difference with better data.’
In one example, the team looked at an income-prediction system and found that it was twice as likely to misclassify female employees as low-income and male employees as high-income.
They found that if they had increased the dataset by a factor of 10, those mistakes would happen 40 percent less often.
In another dataset, the researchers found that a system’s ability to predict intensive care unit (ICU) mortality was less accurate for Asian patients.
the researchers warned existing approaches for reducing discrimination
would make the non-Asian predictions less accurate
Chen says that one of the biggest misconceptions is that more data is always better. Instead, researchers should get more data from those under-represented groups.
view this as a toolbox for helping machine learning engineers figure
out what questions to ask of their data in order to diagnose why their
systems may be making unfair predictions,’ says Sontag.
team will present the paper in December at the annual conference on
Neural Information Processing Systems (NIPS) in Montreal.
Massachusetts Institute of Technology will not just look to churn out single-discipline artificial intelligence graduates, but work to integrate machine learning into other fields — whether that’s history, politics, chemistry, or anything else. The college will equip students and researchers in any discipline to use computing and A.I. to advance their disciplines and vice-versa, as well as to think critically about the human impact of their work
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.
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.