Quantum computing AI lab from Google, NASA and USRA


Google, NASA and the Universities Space Research Association will put a 512 qubit machine from D-Wave at the disposal of researchers around the globe.  The USRA will invite teams of scientists and engineers to share time on the unique supercomputer. The goal is to study how quantum computing might be leveraged to advance machine learning.

Wireless detection of brain trauma


New technology developed at UC Berkeley uses wireless signals to provide real-time, noninvasive diagnoses of brain swelling or bleeding. The device analyzes data from low-energy electromagnetic waves, similar to the kind used to transmit radio and mobile signals.  It is sensitive enough to distinguish between a normal brain and a diseased brain with one single noncontact set of measurements.

Skin mounted electrode arrays measure neural signals


Professor Todd Coleman of UCSD is developing foldable, stretchable electrode arrays that can non-invasively measure neural signals. They can also provide more in-depth analysis by including thermal sensors to monitor skin temperature and light detectors to analyze blood oxygen levels.  The device is powered by micro solar panels and uses antennae to wirelessly transmit or receive data.  Professor Coleman wants to use the device on premature babies to monitor their mental state and detect the onset of seizures that can lead to brain development problems such as epilepsy.

Nanotube sensor detects glucose in saliva


A team led by Mitchell Lerner at the University of Pennsylvania has developed a carbon nanotube based transistor that can detect glucose levels in body fluids, including saliva. The nanotubes are coated with molecules of pyrene-1-boronic acid, which makes them highly sensitive for glucose detection. When exposed to glucose, the nanotube transistor’s current-voltage curve changes, and that change can be measured to indicate the glucose concentration.

Real-time brain feedback for anxiety disorders


fMRI-driven neurofeedback has been used in various contexts, but never applied to the treatment of anxiety.

Yale University researchers used fMRI to display the activity of the orbitofrontal cortex, a brain region just above the eyes, to subjects in real time.  Through a process of trial and error, the subjects learned to control their brain activity.  This neurofeedback led to changes in brain connectivity and increased control over anxiety.  The changes were still present several days after the exercise.

Flexible “skin” heart monitor

Stanford professor Zhenan Bao has developed a flexible, skin-like heart monitor, worn under an adhesive bandage on the wrist.  This non-invasive method could replace intravascular catheters, which create a high risk of infection, making them impractical for newborns and high-risk patients.  An external monitor could give doctors a safer way to gather information about the heart, especially during infant surgeries.  Bao’s team is working with other Stanford researchers to make the device completely wireless.

Haptic hand monitors joint mobility


Ireland’s Tyndall National Institute’s “haptic hand” sensorized glove collects hand movement data to assist doctors’ understanding of arthritis patient mobility. Sensors built into the glove will provide 3-D simulations of joint movement and information on hand stiffness. The glove could potentially also be used to track hand movements in other applications, such as stroke rehab and training of surgeons.

Computer vision algorithms used to diagnose depression


SimSensei software, developed by Stefan Scherer and colleagues at the University of Southern California, combines computer vision algorithms and the psychological model of depression. An on-screen psychologist asks you a series of questions and watches how you physically respond. Using Kinect, the computer vision algorithms build up a very detailed model of your face and body, including your “smile level,” horizontal gaze and vertical gaze, how wide open your eyes are, and whether you are leaning toward or away from the camera. From these markers, SimSensei can determine whether you’re exhibiting signs that indicate depression — gaze aversion, smiling less, and fidgeting.

SOINN artificial brain learns from the internet, applies information


A group at the Tokyo Institute of Technology, led by Dr. Osamu Hasegawa, has advanced SOINN, their machine learning algorithm, which can now use the internet to learn how to perform new tasks. The system, which is under development as an artificial brain for autonomous mental development robots, is currently being used to learn about objects in photos using image searches on the internet. It can also take aspects of other known objects and combine them to make guesses about objects it doesn’t yet recognize.