My colleagues and I have developed a flexible, stretchable electronic device that runs machine-learning algorithms to continuously collect and analyze health data directly on the body. The skin-like sticker, developed in my lab at the University of Chicago's Pritzker School of Molecular Engineering, includes a soft, stretchable computing chip that mimics the human brain. To create this type of device, we turned to electrically conductive polymers that have been used to build semiconductors and transistors. These polymers are made to be stretchable, like a rubber band. Rather than working like a typical computer chip, though, the chip we're working with, called a neuromorphic computing chip, functions more like a human brain.

The Research Brief is a short take about interesting academic work. My colleagues and I have developed a flexible, stretchable electronic device that runs machine-learning algorithms to continuously collect and analyze health data directly on the body. The skinlike sticker, developed in my lab at the University of Chicago's Pritzker School of Molecular Engineering, includes a soft, stretchable computing chip that mimics the human brain. To create this type of device, we turned to electrically conductive polymers that have been used to build semiconductors and transistors. These polymers are made to be stretchable, like a rubber band.

Prof. Sihong Wang shows a single neuromorphic device with three electrodes. Researchers at the University of Chicago's Pritzker School of Molecular Engineering (PME) have developed a flexible, stretchable computing chip that processes information by mimicking the human brain. The device, described in the journal Matter, aims to change the way health data is processed. "With this work we've bridged wearable technology with artificial intelligence and machine learning to create a powerful device which can analyze health data right on our own bodies," said Sihong Wang, a materials scientist and Assistant Professor of Molecular Engineering. Today, getting an in-depth profile about your health requires a visit to a hospital or clinic. In the future, Wang said, people's health could be tracked continuously by wearable electronics that can detect disease even before symptoms appear.

Amazon.com on Thursday said it shifted part of the computing for its Alexa voice assistant to its own custom-designed chips, aiming to make the work faster and cheaper while moving it away from chips supplied by Nvidia. When users of devices such as Amazon's Echo line of smart speakers ask the voice assistant a question, the query is sent to one of Amazon's data centers for several steps of processing. When Amazon's computers spit out an answer, that reply is in a text format that must be translated into audible speech for the voice assistant. Amazon previously handled that computing using chips from Nvidia but now the "majority" of it will happen using its own Inferentia computing chip. First announced in 2018, the Amazon chip is custom designed to speed up large volumes of machine learning tasks such as translating text to speech or recognizing images.

An emerging form of AI known as neuromorphic computing has been used to recognize scents emitted by explosives, chemical weapons, and narcotics. Researchers from Intel and Cornell University made the breakthrough by equipping Intel's neuromorphic test chip Loihi with neural algorithms that mimic what happens in your brain when you smell something. This enabled the system to recognize the smell of each hazardous chemical from just a single sample. The study could pave the way to a vast range of applications of neuromorphic computing, which mimics the brain's basic mechanics to make machine learning more efficient. Intel believes the "electronic nose systems" could be used by airport security to detect weapons and explosives, by police and border control to find narcotics, by robots to monitor gases pimped out into the atmosphere, and by the makers of smoke detectors to improve their products.

You are free to share this article under the Attribution 4.0 International license. Researchers report that they've used a mobile, brain-inspired processor to analyze brain signals from retrospective patient data and successfully predict an average of 69 percent of seizures across all patients with artificial intelligence. The research could help pave the way for personalized seizure prediction for patients with epilepsy. "Our algorithm also allows for instantaneous and easy adjustment, giving patients the flexibility to control how sensitive and in advance the warning is…" With a third of epilepsy patients worldwide currently living with unpredictable seizures that are not adequately controlled through medication or otherwise. This research could dramatically improve the lives of 250,000 Australians and 65 million people worldwide, says Mark Cook, director of the University of Melbourne's Graeme Clark Institute for Biomedical Engineering and director of neurology at St. Vincent's Hospital in Melbourne.